├── .gitignore ├── basic ├── __init__.py ├── __init__.pyc ├── common.pyc ├── text2json.pyc ├── basicfunctions.pyc ├── __pycache__ │ ├── common.cpython-36.pyc │ ├── common.cpython-37.pyc │ ├── __init__.cpython-36.pyc │ ├── __init__.cpython-37.pyc │ └── text2json.cpython-36.pyc ├── common.py ├── text2json2.py ├── text2json.py └── basicfunctions.py ├── .vscode ├── settings.json └── launch.json ├── core ├── __pycache__ │ ├── utils.cpython-37.pyc │ ├── __init__.cpython-37.pyc │ └── mdvrptw.cpython-37.pyc ├── __init__.py ├── utils.py └── mdvrptw.py ├── README.md ├── format_text.py ├── instance_to_csv.py ├── sample_pr01.py ├── sample_pr02.py ├── pr01_2.csv ├── data └── c-mdvrptw │ ├── csv │ ├── pr01.csv │ └── pr02.csv │ └── txt │ ├── pr01.txt │ ├── pr11.txt │ ├── pr07.txt │ ├── pr17.txt │ ├── pr02.txt │ ├── pr12.txt │ ├── pr03.txt │ ├── pr13.txt │ ├── pr08.txt │ ├── pr18.txt │ ├── pr04.txt │ ├── pr14.txt │ ├── pr05.txt │ ├── pr15.txt │ ├── pr09.txt │ ├── pr19.txt │ ├── pr06.txt │ └── pr16.txt ├── kmeans_example.py ├── shakespeare.py └── new_algorithm2.py /.gitignore: -------------------------------------------------------------------------------- 1 | venv/ -------------------------------------------------------------------------------- /basic/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /basic/__init__.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ukarus/mdvrptw-python/HEAD/basic/__init__.pyc -------------------------------------------------------------------------------- /basic/common.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ukarus/mdvrptw-python/HEAD/basic/common.pyc -------------------------------------------------------------------------------- /basic/text2json.pyc: 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https://raw.githubusercontent.com/Ukarus/mdvrptw-python/HEAD/basic/__pycache__/__init__.cpython-37.pyc -------------------------------------------------------------------------------- /basic/__pycache__/text2json.cpython-36.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Ukarus/mdvrptw-python/HEAD/basic/__pycache__/text2json.cpython-36.pyc -------------------------------------------------------------------------------- /core/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import os 3 | 4 | 5 | BASE_DIR = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) 6 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## Requerimientos 2 | 3 | - [Pandas](https://pandas.pydata.org/) 4 | - [Numpy](https://www.numpy.org/) 5 | - [Scikit-learn](https://scikit-learn.org/stable/) 6 | - [Seaborn](https://seaborn.pydata.org/) 7 | - [Matplot](https://matplotlib.org/) 8 | - [Python 3.5+](https://www.python.org/) 9 | 10 | 11 | ## Instalación 12 | 13 | 14 | -------------------------------------------------------------------------------- /format_text.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os 4 | from basic.common import getrootpath 5 | from core.utils import text2json 6 | 7 | def main(): 8 | 9 | instance= 'pr02.txt' 10 | rootpath = getrootpath() 11 | filePath = os.path.join(rootpath,'data','c-mdvrptw','txt') 12 | 13 | #Función para formatear datos en formato json 14 | text2json(instance,filePath) 15 | 16 | 17 | if __name__ == '__main__': 18 | main() -------------------------------------------------------------------------------- /instance_to_csv.py: -------------------------------------------------------------------------------- 1 | 2 | import csv 3 | 4 | instanceName = 'pr02.txt' 5 | textFile = open('./data/c-mdvrptw/txt/%s' % instanceName, 'r') 6 | 7 | 8 | 9 | with open(instanceName.split(".")[0] + '.csv', 'w', newline='') as writeFile: 10 | writer = csv.writer(writeFile) 11 | firstRow = ['x', 'y'] 12 | writer.writerow(firstRow) 13 | for lineCount, line in enumerate(textFile, start=1): 14 | if lineCount >= 6: 15 | values = line.strip().split() 16 | # newLine = values[0] + ',' + values[1] + ',' + values[2] 17 | newRow = [values[1], values[2]] 18 | writer.writerow(newRow) 19 | 20 | textFile.close() 21 | writeFile.close() -------------------------------------------------------------------------------- /sample_pr01.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from core.mdvrptw import run_mdvrptw 4 | 5 | def main(): 6 | 7 | instance_name = 'pr01.txt.json' 8 | 9 | unit_cost = 8.0 10 | init_cost = 60.0 11 | wait_cost = 0.5 12 | delay_cost = 1.5 13 | 14 | ind_size = 25 15 | pop_size = 80 16 | cx_pb = 0.85 17 | mut_pb = 0.01 18 | n_gen = 100 19 | 20 | export_csv = True 21 | 22 | run_mdvrptw( 23 | instance_name=instance_name, 24 | unit_cost=unit_cost, 25 | init_cost=init_cost, 26 | wait_cost=wait_cost, 27 | delay_cost=delay_cost, 28 | ind_size=ind_size, 29 | pop_size=pop_size, 30 | cx_pb=cx_pb, 31 | mut_pb=mut_pb, 32 | n_gen=n_gen, 33 | export_csv=export_csv 34 | ) 35 | 36 | 37 | if __name__ == '__main__': 38 | main() -------------------------------------------------------------------------------- /sample_pr02.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | from core.mdvrptw import run_mdvrptw 4 | 5 | def main(): 6 | 7 | instance_name = 'pr02.txt.json' 8 | 9 | unit_cost = 8.0 10 | init_cost = 60.0 11 | wait_cost = 0.5 12 | delay_cost = 1.5 13 | 14 | ind_size = 25 15 | pop_size = 110 16 | cx_pb = 0.85 17 | mut_pb = 0.01 18 | n_gen = 120 19 | 20 | export_csv = True 21 | 22 | run_mdvrptw( 23 | instance_name=instance_name, 24 | unit_cost=unit_cost, 25 | init_cost=init_cost, 26 | wait_cost=wait_cost, 27 | delay_cost=delay_cost, 28 | ind_size=ind_size, 29 | pop_size=pop_size, 30 | cx_pb=cx_pb, 31 | mut_pb=mut_pb, 32 | n_gen=n_gen, 33 | export_csv=export_csv 34 | ) 35 | 36 | 37 | if __name__ == '__main__': 38 | main() -------------------------------------------------------------------------------- /pr01_2.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | -29.730,64.136 3 | -30.664,5.463 4 | 51.642,5.469 5 | -13.171,69.336 6 | -67.413,68.323 7 | 48.907,6.274 8 | 5.243,22.260 9 | -65.002,77.234 10 | -4.175,-1.569 11 | 23.029,11.639 12 | 25.482,6.287 13 | -42.615,-26.392 14 | -76.672,99.341 15 | -20.673,57.892 16 | -52.039,6.567 17 | -41.376,50.824 18 | -91.943,27.588 19 | -65.118,30.212 20 | 18.597,96.716 21 | -40.942,83.209 22 | -37.756,-33.325 23 | 23.767,29.083 24 | -43.030,20.453 25 | -35.297,-24.896 26 | -54.755,14.368 27 | -49.329,33.374 28 | 57.404,23.822 29 | -22.754,55.408 30 | -56.622,73.340 31 | -38.562,-3.705 32 | -16.779,19.537 33 | -11.560,11.615 34 | -46.545,97.974 35 | 16.229,9.320 36 | 1.294,7.349 37 | -26.404,29.529 38 | 4.352,14.685 39 | -50.665,-23.126 40 | -22.833,-9.814 41 | -71.100,-18.616 42 | -7.849,32.074 43 | 11.877,-24.933 44 | -18.927,-23.730 45 | -11.920,11.755 46 | 29.840,11.633 47 | 12.268,-55.811 48 | -37.933,-21.613 49 | 42.883,-2.966 50 | 4.163,13.559 51 | 21.387,17.105 52 | -36.118,49.097 53 | -31.201,0.235 -------------------------------------------------------------------------------- /data/c-mdvrptw/csv/pr01.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | -29.730,64.136 3 | -30.664,5.463 4 | 51.642,5.469 5 | -13.171,69.336 6 | -67.413,68.323 7 | 48.907,6.274 8 | 5.243,22.260 9 | -65.002,77.234 10 | -4.175,-1.569 11 | 23.029,11.639 12 | 25.482,6.287 13 | -42.615,-26.392 14 | -76.672,99.341 15 | -20.673,57.892 16 | -52.039,6.567 17 | -41.376,50.824 18 | -91.943,27.588 19 | -65.118,30.212 20 | 18.597,96.716 21 | -40.942,83.209 22 | -37.756,-33.325 23 | 23.767,29.083 24 | -43.030,20.453 25 | -35.297,-24.896 26 | -54.755,14.368 27 | -49.329,33.374 28 | 57.404,23.822 29 | -22.754,55.408 30 | -56.622,73.340 31 | -38.562,-3.705 32 | -16.779,19.537 33 | -11.560,11.615 34 | -46.545,97.974 35 | 16.229,9.320 36 | 1.294,7.349 37 | -26.404,29.529 38 | 4.352,14.685 39 | -50.665,-23.126 40 | -22.833,-9.814 41 | -71.100,-18.616 42 | -7.849,32.074 43 | 11.877,-24.933 44 | -18.927,-23.730 45 | -11.920,11.755 46 | 29.840,11.633 47 | 12.268,-55.811 48 | -37.933,-21.613 49 | 42.883,-2.966 50 | 4.163,13.559 51 | 21.387,17.105 52 | -36.118,49.097 53 | -31.201,0.235 54 | -------------------------------------------------------------------------------- /basic/common.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os 4 | 5 | ROOT_PATH = [ 6 | os.path.dirname(os.path.realpath('foodif.py')) 7 | #os.path.realpath("app\\Http\\Controllers\\foodif_vrp") 8 | ] 9 | 10 | ''' 11 | os.path.dirname(os.path.realpath(__file__)) 12 | 'C:\wamp64\www\Foodif2\Plataforma-FoodIf\\app\Http\Controllers\\foodif_vrp' 13 | os.path.join(os.environ["USERPROFILE"], "Documents\FoodIf\\foodif_vrp"), 14 | Ubuntu 15 | ROOT_PATH = [ 16 | os.path.join(os.environ['HOME'], 'Documentos/FoodIfV2/foodif_vrp'), 17 | ] 18 | ''' 19 | 20 | 21 | def getrootpath(paths=ROOT_PATH): 22 | if isinstance(paths, list): 23 | for path in ROOT_PATH: 24 | if os.path.exists(path): 25 | return path 26 | elif isinstance(paths, str): 27 | return paths 28 | else: 29 | pass 30 | 31 | 32 | def makeDirsForFile(filename): 33 | try: 34 | os.makedirs(os.path.split(filename)[0]) 35 | except: 36 | pass 37 | 38 | 39 | def existFile(filename, overwrite=False, displayInfo=True): 40 | if os.path.exists(filename): 41 | if overwrite: 42 | os.remove(filename) 43 | if displayInfo: 44 | print ('File: %s exists. Remove: overwrite old file.' % filename) 45 | return False 46 | else: 47 | if displayInfo: 48 | print ('File: %s exists. Skip: no new file is created.' % filename) 49 | return True 50 | else: 51 | if displayInfo: 52 | print ('File: %s does not exist. Create new file. ' % filename) 53 | return False 54 | -------------------------------------------------------------------------------- /data/c-mdvrptw/csv/pr02.csv: -------------------------------------------------------------------------------- 1 | x,y 2 | 33.588,30.750 3 | 48.828,65.314 4 | 86.176,59.344 5 | 39.270,-33.057 6 | -23.370,86.853 7 | 48.132,95.593 8 | -16.357,93.311 9 | -57.703,-65.601 10 | 7.147,32.684 11 | 42.950,68.701 12 | 37.085,-2.112 13 | 77.759,55.817 14 | -17.462,-56.567 15 | 58.575,59.888 16 | 57.776,15.344 17 | -22.327,36.072 18 | -7.080,30.493 19 | 55.658,60.425 20 | -14.307,11.456 21 | -29.724,24.268 22 | 43.219,0.739 23 | 45.184,35.474 24 | 64.484,2.240 25 | 55.078,72.241 26 | 16.925,15.741 27 | 45.038,-3.723 28 | -76.782,5.939 29 | 36.169,0.256 30 | 29.218,8.936 31 | 65.057,5.225 32 | 42.175,-22.284 33 | 25.574,31.726 34 | 31.561,37.262 35 | 66.498,-54.169 36 | 46.576,-17.938 37 | 65.063,40.875 38 | -2.716,24.768 39 | -40.002,3.870 40 | -73.505,57.043 41 | 81.146,-25.714 42 | 12.006,-7.965 43 | 42.761,38.092 44 | 3.857,-23.181 45 | -7.367,24.390 46 | 35.944,-11.835 47 | 52.075,9.692 48 | 30.725,30.701 49 | 41.223,77.924 50 | 68.884,-40.546 51 | 76.312,86.670 52 | 63.934,78.540 53 | 29.150,-9.961 54 | 85.522,39.954 55 | 31.775,3.870 56 | -20.544,19.086 57 | 55.353,43.817 58 | 35.406,10.278 59 | 25.464,-0.287 60 | 12.396,0.244 61 | 18.359,20.917 62 | 27.960,-15.039 63 | -3.192,19.879 64 | 9.332,-41.351 65 | -20.581,38.177 66 | 27.820,28.729 67 | 59.027,42.310 68 | 56.311,58.734 69 | -51.654,-0.342 70 | 2.576,32.721 71 | 15.131,-3.046 72 | 66.595,34.937 73 | 19.476,20.142 74 | -1.172,20.648 75 | -21.204,13.660 76 | 98.846,1.257 77 | 82.593,72.003 78 | 30.017,-30.896 79 | 33.228,41.663 80 | 39.490,-8.539 81 | 54.498,70.813 82 | 3.058,10.248 83 | 51.831,-16.870 84 | 76.416,11.346 85 | 5.243,0.238 86 | 50.592,34.247 87 | -5.231,-2.081 88 | 24.194,25.836 89 | 33.630,37.585 90 | 31.445,-7.001 91 | 30.707,-28.168 92 | 49.860,1.038 93 | -35.767,14.142 94 | -29.309,4.889 95 | 19.049,41.974 96 | 27.600,13.934 97 | 52.832,50.684 98 | 6.229,10.590 99 | 32.663,44.730 100 | 48.807,48.792 101 | 33.179,-4.968 102 | -------------------------------------------------------------------------------- /.vscode/launch.json: -------------------------------------------------------------------------------- 1 | { 2 | // Use IntelliSense to learn about possible attributes. 3 | // Hover to view descriptions of existing attributes. 4 | // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 5 | "version": "0.2.0", 6 | "configurations": [ 7 | { 8 | "name": "Python: Current File (Integrated Terminal)", 9 | "type": "python", 10 | "request": "launch", 11 | "program": "${file}", 12 | "console": "integratedTerminal" 13 | }, 14 | { 15 | "name": "Python: Remote Attach", 16 | "type": "python", 17 | "request": "attach", 18 | "port": 5678, 19 | "host": "localhost", 20 | "pathMappings": [ 21 | { 22 | "localRoot": "${workspaceFolder}", 23 | "remoteRoot": "." 24 | } 25 | ] 26 | }, 27 | { 28 | "name": "Python: Module", 29 | "type": "python", 30 | "request": "launch", 31 | "module": "enter-your-module-name-here", 32 | "console": "integratedTerminal" 33 | }, 34 | { 35 | "name": "Python: Django", 36 | "type": "python", 37 | "request": "launch", 38 | "program": "${workspaceFolder}/manage.py", 39 | "console": "integratedTerminal", 40 | "args": [ 41 | "runserver", 42 | "--noreload", 43 | "--nothreading" 44 | ], 45 | "django": true 46 | }, 47 | { 48 | "name": "Python: Flask", 49 | "type": "python", 50 | "request": "launch", 51 | "module": "flask", 52 | "env": { 53 | "FLASK_APP": "app.py" 54 | }, 55 | "args": [ 56 | "run", 57 | "--no-debugger", 58 | "--no-reload" 59 | ], 60 | "jinja": true 61 | }, 62 | { 63 | "name": "Python: Current File (External Terminal)", 64 | "type": "python", 65 | "request": "launch", 66 | "program": "${file}", 67 | "console": "externalTerminal" 68 | } 69 | ] 70 | } -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr01.txt: -------------------------------------------------------------------------------- 1 | 6 2 48 4 2 | 500 200 3 | 500 200 4 | 500 200 5 | 500 200 6 | 1 -29.730 64.136 2 12 1 4 1 2 4 8 399 525 7 | 2 -30.664 5.463 7 8 1 4 1 2 4 8 121 299 8 | 3 51.642 5.469 21 16 1 4 1 2 4 8 389 483 9 | 4 -13.171 69.336 24 5 1 4 1 2 4 8 204 304 10 | 5 -67.413 68.323 1 12 1 4 1 2 4 8 317 458 11 | 6 48.907 6.274 17 5 1 4 1 2 4 8 160 257 12 | 7 5.243 22.260 6 13 1 4 1 2 4 8 170 287 13 | 8 -65.002 77.234 5 20 1 4 1 2 4 8 215 321 14 | 9 -4.175 -1.569 7 13 1 4 1 2 4 8 80 233 15 | 10 23.029 11.639 1 18 1 4 1 2 4 8 90 206 16 | 11 25.482 6.287 4 7 1 4 1 2 4 8 397 525 17 | 12 -42.615 -26.392 10 6 1 4 1 2 4 8 271 420 18 | 13 -76.672 99.341 2 9 1 4 1 2 4 8 108 266 19 | 14 -20.673 57.892 16 9 1 4 1 2 4 8 340 462 20 | 15 -52.039 6.567 23 4 1 4 1 2 4 8 226 377 21 | 16 -41.376 50.824 18 25 1 4 1 2 4 8 446 604 22 | 17 -91.943 27.588 3 5 1 4 1 2 4 8 444 566 23 | 18 -65.118 30.212 15 17 1 4 1 2 4 8 434 557 24 | 19 18.597 96.716 13 3 1 4 1 2 4 8 319 460 25 | 20 -40.942 83.209 10 16 1 4 1 2 4 8 192 312 26 | 21 -37.756 -33.325 4 25 1 4 1 2 4 8 414 572 27 | 22 23.767 29.083 23 21 1 4 1 2 4 8 371 462 28 | 23 -43.030 20.453 20 14 1 4 1 2 4 8 378 472 29 | 24 -35.297 -24.896 10 19 1 4 1 2 4 8 308 477 30 | 25 -54.755 14.368 4 14 1 4 1 2 4 8 329 444 31 | 26 -49.329 33.374 2 6 1 4 1 2 4 8 269 377 32 | 27 57.404 23.822 23 16 1 4 1 2 4 8 398 494 33 | 28 -22.754 55.408 6 9 1 4 1 2 4 8 257 416 34 | 29 -56.622 73.340 8 20 1 4 1 2 4 8 198 294 35 | 30 -38.562 -3.705 10 13 1 4 1 2 4 8 375 467 36 | 31 -16.779 19.537 7 10 1 4 1 2 4 8 200 338 37 | 32 -11.560 11.615 1 16 1 4 1 2 4 8 456 632 38 | 33 -46.545 97.974 21 19 1 4 1 2 4 8 72 179 39 | 34 16.229 9.320 6 22 1 4 1 2 4 8 182 282 40 | 35 1.294 7.349 4 14 1 4 1 2 4 8 159 306 41 | 36 -26.404 29.529 13 10 1 4 1 2 4 8 321 500 42 | 37 4.352 14.685 9 11 1 4 1 2 4 8 322 430 43 | 38 -50.665 -23.126 22 15 1 4 1 2 4 8 443 564 44 | 39 -22.833 -9.814 22 13 1 4 1 2 4 8 207 348 45 | 40 -71.100 -18.616 18 15 1 4 1 2 4 8 457 588 46 | 41 -7.849 32.074 10 8 1 4 1 2 4 8 203 382 47 | 42 11.877 -24.933 25 22 1 4 1 2 4 8 75 167 48 | 43 -18.927 -23.730 23 24 1 4 1 2 4 8 459 598 49 | 44 -11.920 11.755 4 3 1 4 1 2 4 8 174 332 50 | 45 29.840 11.633 9 25 1 4 1 2 4 8 130 225 51 | 46 12.268 -55.811 17 19 1 4 1 2 4 8 169 283 52 | 47 -37.933 -21.613 10 21 1 4 1 2 4 8 115 232 53 | 48 42.883 -2.966 17 10 1 4 1 2 4 8 414 531 54 | 49 4.163 13.559 0 0 0 0 0 1000 55 | 50 21.387 17.105 0 0 0 0 0 1000 56 | 51 -36.118 49.097 0 0 0 0 0 1000 57 | 52 -31.201 0.235 0 0 0 0 0 1000 58 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr11.txt: -------------------------------------------------------------------------------- 1 | 6 1 48 4 2 | 500 200 3 | 500 200 4 | 500 200 5 | 500 200 6 | 1 -29.730 64.136 2 12 1 4 1 2 4 8 276 548 7 | 2 -30.664 5.463 7 8 1 4 1 2 4 8 98 438 8 | 3 51.642 5.469 21 16 1 4 1 2 4 8 91 330 9 | 4 -13.171 69.336 24 5 1 4 1 2 4 8 177 505 10 | 5 -67.413 68.323 1 12 1 4 1 2 4 8 282 581 11 | 6 48.907 6.274 17 5 1 4 1 2 4 8 262 445 12 | 7 5.243 22.260 6 13 1 4 1 2 4 8 223 458 13 | 8 -65.002 77.234 5 20 1 4 1 2 4 8 109 352 14 | 9 -4.175 -1.569 7 13 1 4 1 2 4 8 262 588 15 | 10 23.029 11.639 1 18 1 4 1 2 4 8 150 447 16 | 11 25.482 6.287 4 7 1 4 1 2 4 8 214 450 17 | 12 -42.615 -26.392 10 6 1 4 1 2 4 8 293 639 18 | 13 -76.672 99.341 2 9 1 4 1 2 4 8 157 452 19 | 14 -20.673 57.892 16 9 1 4 1 2 4 8 172 458 20 | 15 -52.039 6.567 23 4 1 4 1 2 4 8 233 442 21 | 16 -41.376 50.824 18 25 1 4 1 2 4 8 152 333 22 | 17 -91.943 27.588 3 5 1 4 1 2 4 8 158 385 23 | 18 -65.118 30.212 15 17 1 4 1 2 4 8 106 456 24 | 19 18.597 96.716 13 3 1 4 1 2 4 8 180 363 25 | 20 -40.942 83.209 10 16 1 4 1 2 4 8 62 297 26 | 21 -37.756 -33.325 4 25 1 4 1 2 4 8 110 468 27 | 22 23.767 29.083 23 21 1 4 1 2 4 8 242 553 28 | 23 -43.030 20.453 20 14 1 4 1 2 4 8 91 376 29 | 24 -35.297 -24.896 10 19 1 4 1 2 4 8 246 487 30 | 25 -54.755 14.368 4 14 1 4 1 2 4 8 202 382 31 | 26 -49.329 33.374 2 6 1 4 1 2 4 8 200 446 32 | 27 57.404 23.822 23 16 1 4 1 2 4 8 161 377 33 | 28 -22.754 55.408 6 9 1 4 1 2 4 8 247 579 34 | 29 -56.622 73.340 8 20 1 4 1 2 4 8 73 407 35 | 30 -38.562 -3.705 10 13 1 4 1 2 4 8 250 442 36 | 31 -16.779 19.537 7 10 1 4 1 2 4 8 125 451 37 | 32 -11.560 11.615 1 16 1 4 1 2 4 8 167 368 38 | 33 -46.545 97.974 21 19 1 4 1 2 4 8 162 506 39 | 34 16.229 9.320 6 22 1 4 1 2 4 8 75 266 40 | 35 1.294 7.349 4 14 1 4 1 2 4 8 182 507 41 | 36 -26.404 29.529 13 10 1 4 1 2 4 8 79 289 42 | 37 4.352 14.685 9 11 1 4 1 2 4 8 274 558 43 | 38 -50.665 -23.126 22 15 1 4 1 2 4 8 267 578 44 | 39 -22.833 -9.814 22 13 1 4 1 2 4 8 80 392 45 | 40 -71.100 -18.616 18 15 1 4 1 2 4 8 246 570 46 | 41 -7.849 32.074 10 8 1 4 1 2 4 8 150 419 47 | 42 11.877 -24.933 25 22 1 4 1 2 4 8 74 359 48 | 43 -18.927 -23.730 23 24 1 4 1 2 4 8 215 555 49 | 44 -11.920 11.755 4 3 1 4 1 2 4 8 232 510 50 | 45 29.840 11.633 9 25 1 4 1 2 4 8 130 408 51 | 46 12.268 -55.811 17 19 1 4 1 2 4 8 271 482 52 | 47 -37.933 -21.613 10 21 1 4 1 2 4 8 176 495 53 | 48 42.883 -2.966 17 10 1 4 1 2 4 8 262 521 54 | 49 4.163 13.559 0 0 0 0 0 1000 55 | 50 21.387 17.105 0 0 0 0 0 1000 56 | 51 -36.118 49.097 0 0 0 0 0 1000 57 | 52 -31.201 0.235 0 0 0 0 0 1000 58 | -------------------------------------------------------------------------------- /basic/text2json2.py: -------------------------------------------------------------------------------- 1 | 2 | def text2json(customize=False): 3 | def __distance(customer1, customer2): 4 | return ((customer1['coordinates']['x'] - customer2['coordinates']['x'])**2 + (customer1['coordinates']['y'] - customer2['coordinates']['y'])**2)**0.5 5 | if customize: 6 | textDataDir = os.path.join(BASE_DIR, 'data', 'text_customize') 7 | jsonDataDir = os.path.join(BASE_DIR, 'data', 'json_customize') 8 | else: 9 | textDataDir = os.path.join(BASE_DIR, 'data', 'text') 10 | jsonDataDir = os.path.join(BASE_DIR, 'data', 'json') 11 | for textFile in map(lambda textFilename: os.path.join(textDataDir, textFilename), fnmatch.filter(os.listdir(textDataDir), '*.txt')): 12 | jsonData = {} 13 | with open(textFile) as f: 14 | for lineNum, line in enumerate(f, start=1): 15 | if lineNum in [2, 3, 4, 6, 7, 8, 9]: 16 | pass 17 | elif lineNum == 1: 18 | # 19 | jsonData['instance_name'] = line.strip() 20 | elif lineNum == 5: 21 | # , 22 | values = line.strip().split() 23 | jsonData['max_vehicle_number'] = int(values[0]) 24 | jsonData['vehicle_capacity'] = float(values[1]) 25 | elif lineNum == 10: 26 | # Custom number = 0, deport 27 | # , , , , , , 28 | values = line.strip().split() 29 | jsonData['deport'] = { 30 | 'coordinates': { 31 | 'x': float(values[1]), 32 | 'y': float(values[2]), 33 | }, 34 | 'demand': float(values[3]), 35 | 'ready_time': float(values[4]), 36 | 'due_time': float(values[5]), 37 | 'service_time': float(values[6]), 38 | } 39 | else: 40 | # , , , , , , 41 | values = line.strip().split() 42 | jsonData['customer_%s' % values[0]] = { 43 | 'coordinates': { 44 | 'x': float(values[1]), 45 | 'y': float(values[2]), 46 | }, 47 | 'demand': float(values[3]), 48 | 'ready_time': float(values[4]), 49 | 'due_time': float(values[5]), 50 | 'service_time': float(values[6]), 51 | } 52 | customers = ['deport'] + ['customer_%d' % x for x in range(1, 101)] 53 | jsonData['distance_matrix'] = [[__distance(jsonData[customer1], jsonData[customer2]) for customer1 in customers] for customer2 in customers] 54 | jsonFilename = '%s.json' % jsonData['instance_name'] 55 | jsonPathname = os.path.join(jsonDataDir, jsonFilename) 56 | print 'Write to file: %s' % jsonPathname 57 | makeDirsForFile(pathname=jsonPathname) 58 | with open(jsonPathname, 'w') as f: 59 | dump(jsonData, f, sort_keys=True, indent=4, separators=(',', ': ')) -------------------------------------------------------------------------------- /basic/text2json.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | import random 3 | import os 4 | import json 5 | from basic.common import * 6 | 7 | def euclideanDistance(customer1, customer2): 8 | return ((customer1['coordinates']['x'] - customer2['coordinates']['x'])**2 + (customer1['coordinates']['y'] - customer2['coordinates']['y'])**2)**0.5 9 | 10 | 11 | def distance_matrix(depots, customers, jsonData): 12 | jsonResponse = {} 13 | distanceMatrix = [] 14 | subDistance = [] 15 | for depot in depots: 16 | customerswDepot = [depot] + customers 17 | for customer1 in customerswDepot: 18 | for customer2 in customerswDepot: 19 | distance = euclideanDistance(jsonData[customer2], jsonData[customer1]) 20 | subDistance.append(distance) 21 | distanceMatrix.append(subDistance) 22 | subDistance = [] 23 | jsonResponse['%s'%depot] = distanceMatrix 24 | distanceMatrix = [] 25 | customerswDepot = [] 26 | return jsonResponse 27 | 28 | def text2json(instance, filePath): 29 | def __distance(customer1, customer2): 30 | return ((customer1['coordinates']['x'] - customer2['coordinates']['x'])**2 + (customer1['coordinates']['y'] - customer2['coordinates']['y'])**2)**0.5 31 | 32 | textFile = os.path.join(filePath, instance) 33 | jsonData = {} 34 | depots = 0 35 | vehicles = 0 36 | customers = 0 37 | depotsCounter = 1 38 | with open(textFile) as f: 39 | for lineCount, line in enumerate(f, start=1): 40 | if lineCount == 1: 41 | values = line.strip().split() 42 | vehicles = int(values[1]) 43 | customers = int(values[2]) 44 | nDepots = int(values[3]) 45 | jsonData["vehicles_number"] = vehicles 46 | jsonData["depots"] = nDepots 47 | jsonData["customers"] = customers 48 | elif lineCount in range(2, (nDepots+2)): 49 | values = line.strip().split() 50 | jsonData["depot_%s" % str(customers+depotsCounter)] = { 51 | "max_duration" : values[0], 52 | "capacity" : values[1] 53 | } 54 | depotsCounter += 1 55 | elif lineCount in range( (nDepots+2), (customers+nDepots+2)): 56 | values = line.strip().split() 57 | jsonData["customer_%s" %values[0]]={ 58 | "coordinates":{ 59 | "x": float(values[1]), 60 | "y": float(values[2]) 61 | }, 62 | "service_duration": values[3], 63 | "demand" : values[4], 64 | "earliest_time" : values[11], 65 | "latest_time": values[12] 66 | } 67 | else: 68 | values = line.strip().split() 69 | jsonData["depot_%s" %values[0]].update({ 70 | "coordinates":{ 71 | "x": float(values[1]), 72 | "y": float(values[2]) 73 | }, 74 | "latest_time": values[8] 75 | }) 76 | depots = ['depot_%d' % x for x in range(customers+1, customers+nDepots+1)] 77 | customers = ['customer_%d' % x for x in range(1, customers+1)] 78 | asdf= distance_matrix(depots, customers, jsonData) 79 | jsonData['distance_matrix'] = asdf 80 | """ jsonData['distance_matrix'] = [[__distance(jsonData[customer1], jsonData[customer2]) for customer1 in customers] for customer2 in customers] """ 81 | 82 | jsonFilename = '%s.json' % instance.split(".")[0] 83 | jsonFile = os.path.join(filePath, jsonFilename) 84 | print ('Write to file: %s' % jsonFile) 85 | makeDirsForFile(jsonFile) 86 | with open(jsonFile, 'w') as f: 87 | json.dump(jsonData, f, sort_keys=True, indent=4, separators=(',', ': ')) -------------------------------------------------------------------------------- /kmeans_example.py: -------------------------------------------------------------------------------- 1 | # Dependencies 2 | 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.cluster import KMeans 6 | from sklearn.preprocessing import LabelEncoder 7 | from sklearn.preprocessing import MinMaxScaler 8 | import seaborn as sns 9 | import matplotlib.pyplot as plt 10 | # %matplotlib inline 11 | 12 | pr01 = pd.read_csv(r'C:\Users\juanj\Documents\Trabajo de Titulo 2\algorithm\pr01_2.csv') 13 | x = np.array(pr01) 14 | clusters = np.array([[4.163, 13.559], [21.387, 17.105], [-36.118, 49.097], [-31.201, 0.235]]) 15 | kmeans = KMeans(n_clusters=4, init=clusters, n_init=1).fit(x) 16 | print(kmeans.labels_) 17 | print(kmeans.cluster_centers_) 18 | 19 | # example2 = np.array( [[5,3], [10,15], [15,12], [24,10], [30,45], [85,70], [71,80], [60,78], [55,52], [80,91]]) 20 | # means = KMeans(n_clusters=2, init=np.array([ [5,3], [10, 15] ]), n_init=1).fit(example2) 21 | # means = KMeans(n_clusters=2).fit(example2) 22 | # print(means.labels_) 23 | # print(means.cluster_centers_) 24 | # X = np.array([[1, 2], [1, 4], [1, 0], [10, 2], [10, 4], [10, 0]]) 25 | # kmeans = KMeans(n_clusters=2, random_state=0).fit(X) 26 | # print(kmeans.labels_) 27 | # print(kmeans.predict([[0, 0], [12, 3]])) 28 | # print(kmeans.cluster_centers_) 29 | 30 | ''' 31 | train_url = "http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/train.csv" 32 | train = pd.read_csv(train_url) 33 | test_url = "http://s3.amazonaws.com/assets.datacamp.com/course/Kaggle/test.csv" 34 | test = pd.read_csv(test_url) 35 | 36 | # print("***** Train_Set *****") 37 | # print(train.head()) 38 | # print("\n") 39 | # print("***** Test_Set *****") 40 | # print(test.head()) 41 | 42 | # print("***** Train_Set *****") 43 | # print(train.describe()) 44 | # print("\n") 45 | # print("***** Test_Set *****") 46 | # print(test.describe()) 47 | 48 | # print(train.columns.values) 49 | # For the train set 50 | train.isna().head() 51 | # For the test set 52 | test.isna().head() 53 | 54 | 55 | # print("*****In the train set*****") 56 | # print(train.isna().sum()) 57 | # print("\n") 58 | # print("*****In the test set*****") 59 | # print(test.isna().sum()) 60 | 61 | # Fill missing values with mean column values in the train set 62 | train.fillna(train.mean(), inplace=True) 63 | 64 | # Fill missing values with mean column values in the test set 65 | test.fillna(test.mean(), inplace=True) 66 | 67 | 68 | print("*****In the train set*****") 69 | print(train.isna().sum()) 70 | print("\n") 71 | print("*****In the test set*****") 72 | print(test.isna().sum()) 73 | 74 | # print(train['Ticket'].head()) 75 | 76 | print(train[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean().sort_values(by='Survived', ascending=False)) 77 | print(train[["Sex", "Survived"]].groupby(['Sex'], as_index=False).mean().sort_values(by='Survived', ascending=False)) 78 | print(train[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False).mean().sort_values(by='Survived', ascending=False)) 79 | 80 | # g = sns.FacetGrid(train, col='Survived') 81 | # g.map(plt.hist, 'Age', bins=20) 82 | 83 | print(train.info()) 84 | train = train.drop(['Name','Ticket', 'Cabin','Embarked'], axis=1) 85 | test = test.drop(['Name','Ticket', 'Cabin','Embarked'], axis=1) 86 | 87 | labelEncoder = LabelEncoder() 88 | labelEncoder.fit(train['Sex']) 89 | labelEncoder.fit(test['Sex']) 90 | train['Sex'] = labelEncoder.transform(train['Sex']) 91 | test['Sex'] = labelEncoder.transform(test['Sex']) 92 | 93 | # Let's investigate if you have non-numeric data left 94 | 95 | # train.info() 96 | 97 | x = np.array(train.drop(['Survived'], 1).astype(float)) 98 | y = np.array(train['Survived']) 99 | 100 | print("\n") 101 | print(" **** After Survived column removed ****") 102 | train.info() 103 | # test.info() 104 | print(x) 105 | scaler = MinMaxScaler() 106 | X_scaled = scaler.fit_transform(x) 107 | kmeans = KMeans(n_clusters=2, max_iter=600, algorithm = 'auto') # You want cluster the passenger records into 2: Survived or Not survived 108 | kmeans.fit(X_scaled) 109 | 110 | print(X_scaled) 111 | correct = 0 112 | for i in range(len(x)): 113 | predict_me = np.array(x[i].astype(float)) 114 | predict_me = predict_me.reshape(-1, len(predict_me)) 115 | prediction = kmeans.predict(predict_me) 116 | if prediction[0] == y[i]: 117 | correct += 1 118 | 119 | print(correct/len(x)) 120 | ''' -------------------------------------------------------------------------------- /core/utils.py: -------------------------------------------------------------------------------- 1 | import random, os, json, math 2 | from basic.common import * 3 | 4 | def euclideanDistance(x1, y1, x2, y2): 5 | return round(math.sqrt( pow(x2 - x1, 2) + pow(y2 - y1, 2)), 3) 6 | 7 | def nodeToCoordinates(node, depotsID, jsonData): 8 | x = 0.0 9 | y = 0.0 10 | if node in depotsID: 11 | x = jsonData["depot_%i" % node]["coordinates"]["x"] 12 | y = jsonData["depot_%i" % node]["coordinates"]["y"] 13 | else: 14 | x = jsonData["customer_%i" % node]["coordinates"]["x"] 15 | y = jsonData["customer_%i" % node]["coordinates"]["y"] 16 | return x, y 17 | 18 | 19 | def distanceMatrix(depotsID, customersID, jsonData): 20 | distance_matrix = [] 21 | subDistance = [] 22 | allNodes = customersID + depotsID 23 | for node1 in allNodes: 24 | x1, y1 = nodeToCoordinates(node1, depotsID, jsonData) 25 | for node2 in allNodes: 26 | x2, y2 = nodeToCoordinates(node2, depotsID, jsonData) 27 | subDistance.append(euclideanDistance(x1, y1, x2, y2)) 28 | distance_matrix.append(subDistance) 29 | subDistance = [] 30 | return distance_matrix 31 | 32 | 33 | def text2json(instance, filePath): 34 | textFile = os.path.join(filePath, instance) 35 | jsonData = {} 36 | numberOfDepots = 0 37 | numberOfVehicles = 0 38 | numberOfCustomers = 0 39 | depotsCounter = 1 40 | with open(textFile) as f: 41 | for lineCount, line in enumerate(f, start=1): 42 | if lineCount == 1: 43 | values = line.strip().split() 44 | numberOfVehicles = int(values[1]) 45 | numberOfCustomers = int(values[2]) 46 | numberOfDepots = int(values[3]) 47 | jsonData["number_of_vehicles"] = numberOfVehicles 48 | jsonData["number_of_customers"] = numberOfCustomers 49 | jsonData["number_of_depots"] = numberOfDepots 50 | # desde la linea 2 hasta la cantidad de depositos 51 | elif lineCount in range(2, (numberOfDepots + 2)): 52 | values = line.strip().split() 53 | jsonData["depot_%s" % str(numberOfCustomers + depotsCounter)] = { 54 | "max_route_duration" : int(values[0]), 55 | "max_vehicle_load" : int(values[1]) 56 | } 57 | depotsCounter += 1 58 | elif lineCount in range( ( numberOfDepots + 2), (numberOfCustomers + numberOfDepots + 2)): 59 | values = line.strip().split() 60 | jsonData["customer_%s" %values[0]]={ 61 | "coordinates":{ 62 | "x": float(values[1]), 63 | "y": float(values[2]) 64 | }, 65 | "service_duration": int(values[3]), 66 | "demand" : int(values[4]), 67 | "ready_time" : int(values[11]), 68 | "due_time": int(values[12]) 69 | } 70 | else: 71 | values = line.strip().split() 72 | jsonData["depot_%s" %values[0]].update({ 73 | "coordinates":{ 74 | "x": float(values[1]), 75 | "y": float(values[2]) 76 | }, 77 | "latest_time": int(values[8]) 78 | }) 79 | depots = ['depot_%d' % x for x in range(numberOfCustomers + 1, numberOfCustomers + numberOfDepots + 1)] 80 | customers = ['customer_%d' % x for x in range(1, numberOfCustomers + 1)] 81 | depotsID = [x for x in range(numberOfCustomers + 1, numberOfCustomers + numberOfDepots + 1)] 82 | customersID = [ x for x in range(1, numberOfCustomers + 1)] 83 | distance_matrix = distanceMatrix(depotsID, customersID, jsonData) 84 | jsonData['distance_matrix'] = distance_matrix 85 | 86 | jsonFilename = '%s.json' % instance 87 | jsonFile = os.path.join(filePath, jsonFilename) 88 | print ('Write to file: %s' % jsonFile) 89 | makeDirsForFile(jsonFile) 90 | with open(jsonFile, 'w') as f: 91 | json.dump(jsonData, f, sort_keys=True, indent=4, separators=(',', ': ')) 92 | 93 | 94 | 95 | # jsonData['distance_matrix'] = [[__distance(jsonData[customer1], jsonData[customer2]) for customer1 in customers] for customer2 in customers] -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr07.txt: -------------------------------------------------------------------------------- 1 | 6 2 72 6 2 | 500 200 3 | 500 200 4 | 500 200 5 | 500 200 6 | 500 200 7 | 500 200 8 | 1 -92.700 -59.180 8 20 1 6 1 2 4 8 16 32 218 308 9 | 2 71.179 12.543 15 6 1 6 1 2 4 8 16 32 129 244 10 | 3 31.537 66.638 20 19 1 6 1 2 4 8 16 32 475 593 11 | 4 -4.694 25.537 7 10 1 6 1 2 4 8 16 32 230 341 12 | 5 -30.194 67.773 13 18 1 6 1 2 4 8 16 32 159 330 13 | 6 12.677 -57.471 6 1 1 6 1 2 4 8 16 32 390 561 14 | 7 -32.355 -20.966 5 15 1 6 1 2 4 8 16 32 389 538 15 | 8 19.910 48.975 1 23 1 6 1 2 4 8 16 32 89 260 16 | 9 13.202 -19.135 12 13 1 6 1 2 4 8 16 32 454 628 17 | 10 54.877 -41.168 18 12 1 6 1 2 4 8 16 32 306 441 18 | 11 15.063 -25.171 25 7 1 6 1 2 4 8 16 32 391 565 19 | 12 -50.598 -16.418 14 25 1 6 1 2 4 8 16 32 106 254 20 | 13 -29.730 17.078 18 5 1 6 1 2 4 8 16 32 460 613 21 | 14 17.542 1.575 13 1 1 6 1 2 4 8 16 32 76 240 22 | 15 11.127 77.216 6 25 1 6 1 2 4 8 16 32 366 523 23 | 16 33.752 71.259 14 10 1 6 1 2 4 8 16 32 270 377 24 | 17 -56.012 -10.394 10 2 1 6 1 2 4 8 16 32 78 235 25 | 18 57.874 -16.290 18 7 1 6 1 2 4 8 16 32 251 385 26 | 19 10.718 -18.787 8 11 1 6 1 2 4 8 16 32 475 597 27 | 20 53.088 -18.750 6 4 1 6 1 2 4 8 16 32 229 340 28 | 21 1.569 7.532 2 6 1 6 1 2 4 8 16 32 203 342 29 | 22 31.531 48.944 4 15 1 6 1 2 4 8 16 32 388 521 30 | 23 -66.833 -37.854 4 7 1 6 1 2 4 8 16 32 425 544 31 | 24 -70.740 62.244 23 8 1 6 1 2 4 8 16 32 333 503 32 | 25 32.538 23.096 12 10 1 6 1 2 4 8 16 32 394 535 33 | 26 -51.453 -36.444 24 9 1 6 1 2 4 8 16 32 380 503 34 | 27 36.456 -22.638 17 4 1 6 1 2 4 8 16 32 160 318 35 | 28 -31.207 43.494 18 6 1 6 1 2 4 8 16 32 313 479 36 | 29 -10.388 34.491 25 22 1 6 1 2 4 8 16 32 60 154 37 | 30 14.722 -10.834 22 17 1 6 1 2 4 8 16 32 267 409 38 | 31 47.095 -21.387 10 9 1 6 1 2 4 8 16 32 281 438 39 | 32 43.781 34.766 25 25 1 6 1 2 4 8 16 32 217 358 40 | 33 53.546 -67.487 21 10 1 6 1 2 4 8 16 32 222 375 41 | 34 26.801 46.515 21 18 1 6 1 2 4 8 16 32 197 324 42 | 35 63.385 11.981 16 21 1 6 1 2 4 8 16 32 111 237 43 | 36 47.192 -5.475 23 10 1 6 1 2 4 8 16 32 247 358 44 | 37 -16.315 -11.267 21 23 1 6 1 2 4 8 16 32 397 522 45 | 38 78.900 17.651 15 23 1 6 1 2 4 8 16 32 130 308 46 | 39 79.822 22.272 7 11 1 6 1 2 4 8 16 32 116 291 47 | 40 12.878 16.919 20 1 1 6 1 2 4 8 16 32 324 473 48 | 41 -67.981 -3.754 6 23 1 6 1 2 4 8 16 32 255 388 49 | 42 9.198 -18.597 16 16 1 6 1 2 4 8 16 32 123 214 50 | 43 -35.950 -19.141 10 10 1 6 1 2 4 8 16 32 281 422 51 | 44 28.766 45.276 7 12 1 6 1 2 4 8 16 32 247 426 52 | 45 11.469 68.231 20 12 1 6 1 2 4 8 16 32 173 285 53 | 46 -22.760 45.496 9 3 1 6 1 2 4 8 16 32 341 481 54 | 47 -65.674 -23.120 12 22 1 6 1 2 4 8 16 32 322 436 55 | 48 7.239 1.599 10 21 1 6 1 2 4 8 16 32 413 534 56 | 49 -29.785 -11.285 19 13 1 6 1 2 4 8 16 32 275 374 57 | 50 -89.050 16.211 6 15 1 6 1 2 4 8 16 32 399 534 58 | 51 -46.887 -3.363 14 13 1 6 1 2 4 8 16 32 64 192 59 | 52 -14.972 30.621 23 20 1 6 1 2 4 8 16 32 182 301 60 | 53 -17.035 49.774 8 10 1 6 1 2 4 8 16 32 456 602 61 | 54 31.635 53.619 10 25 1 6 1 2 4 8 16 32 111 206 62 | 55 -3.577 13.342 14 7 1 6 1 2 4 8 16 32 389 513 63 | 56 33.008 58.960 3 15 1 6 1 2 4 8 16 32 180 304 64 | 57 -92.950 63.263 25 2 1 6 1 2 4 8 16 32 154 296 65 | 58 -9.137 -22.931 21 23 1 6 1 2 4 8 16 32 403 495 66 | 59 -39.960 6.195 5 5 1 6 1 2 4 8 16 32 141 306 67 | 60 28.430 -19.214 2 25 1 6 1 2 4 8 16 32 141 234 68 | 61 -28.540 -3.485 3 9 1 6 1 2 4 8 16 32 377 534 69 | 62 31.415 36.859 21 2 1 6 1 2 4 8 16 32 179 270 70 | 63 -49.426 60.602 1 24 1 6 1 2 4 8 16 32 333 434 71 | 64 -72.827 -27.765 25 13 1 6 1 2 4 8 16 32 258 351 72 | 65 60.083 -45.905 21 20 1 6 1 2 4 8 16 32 426 569 73 | 66 10.870 -3.900 21 13 1 6 1 2 4 8 16 32 67 198 74 | 67 25.122 7.672 25 15 1 6 1 2 4 8 16 32 108 230 75 | 68 -46.997 -17.474 14 4 1 6 1 2 4 8 16 32 181 299 76 | 69 16.058 33.020 20 20 1 6 1 2 4 8 16 32 235 407 77 | 70 25.409 -11.700 14 8 1 6 1 2 4 8 16 32 444 586 78 | 71 68.323 -5.145 11 19 1 6 1 2 4 8 16 32 424 553 79 | 72 -13.104 62.158 25 20 1 6 1 2 4 8 16 32 269 441 80 | 73 42.395 -8.344 0 0 0 0 0 1000 81 | 74 -42.175 -14.554 0 0 0 0 0 1000 82 | 75 16.034 40.726 0 0 0 0 0 1000 83 | 76 -14.639 29.633 0 0 0 0 0 1000 84 | 77 16.049 -3.934 0 0 0 0 0 1000 85 | 78 46.112 12.430 0 0 0 0 0 1000 86 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr17.txt: -------------------------------------------------------------------------------- 1 | 6 1 72 6 2 | 500 200 3 | 500 200 4 | 500 200 5 | 500 200 6 | 500 200 7 | 500 200 8 | 1 -92.700 -59.180 8 20 1 6 1 2 4 8 16 32 94 327 9 | 2 71.179 12.543 15 6 1 6 1 2 4 8 16 32 206 531 10 | 3 31.537 66.638 20 19 1 6 1 2 4 8 16 32 294 651 11 | 4 -4.694 25.537 7 10 1 6 1 2 4 8 16 32 261 563 12 | 5 -30.194 67.773 13 18 1 6 1 2 4 8 16 32 85 406 13 | 6 12.677 -57.471 6 1 1 6 1 2 4 8 16 32 87 309 14 | 7 -32.355 -20.966 5 15 1 6 1 2 4 8 16 32 104 439 15 | 8 19.910 48.975 1 23 1 6 1 2 4 8 16 32 226 449 16 | 9 13.202 -19.135 12 13 1 6 1 2 4 8 16 32 270 551 17 | 10 54.877 -41.168 18 12 1 6 1 2 4 8 16 32 268 599 18 | 11 15.063 -25.171 25 7 1 6 1 2 4 8 16 32 127 351 19 | 12 -50.598 -16.418 14 25 1 6 1 2 4 8 16 32 139 340 20 | 13 -29.730 17.078 18 5 1 6 1 2 4 8 16 32 126 481 21 | 14 17.542 1.575 13 1 1 6 1 2 4 8 16 32 192 453 22 | 15 11.127 77.216 6 25 1 6 1 2 4 8 16 32 155 467 23 | 16 33.752 71.259 14 10 1 6 1 2 4 8 16 32 195 379 24 | 17 -56.012 -10.394 10 2 1 6 1 2 4 8 16 32 151 403 25 | 18 57.874 -16.290 18 7 1 6 1 2 4 8 16 32 298 578 26 | 19 10.718 -18.787 8 11 1 6 1 2 4 8 16 32 272 490 27 | 20 53.088 -18.750 6 4 1 6 1 2 4 8 16 32 221 529 28 | 21 1.569 7.532 2 6 1 6 1 2 4 8 16 32 196 544 29 | 22 31.531 48.944 4 15 1 6 1 2 4 8 16 32 219 411 30 | 23 -66.833 -37.854 4 7 1 6 1 2 4 8 16 32 73 332 31 | 24 -70.740 62.244 23 8 1 6 1 2 4 8 16 32 82 303 32 | 25 32.538 23.096 12 10 1 6 1 2 4 8 16 32 282 630 33 | 26 -51.453 -36.444 24 9 1 6 1 2 4 8 16 32 195 550 34 | 27 36.456 -22.638 17 4 1 6 1 2 4 8 16 32 128 405 35 | 28 -31.207 43.494 18 6 1 6 1 2 4 8 16 32 186 537 36 | 29 -10.388 34.491 25 22 1 6 1 2 4 8 16 32 72 335 37 | 30 14.722 -10.834 22 17 1 6 1 2 4 8 16 32 178 512 38 | 31 47.095 -21.387 10 9 1 6 1 2 4 8 16 32 180 433 39 | 32 43.781 34.766 25 25 1 6 1 2 4 8 16 32 144 430 40 | 33 53.546 -67.487 21 10 1 6 1 2 4 8 16 32 113 400 41 | 34 26.801 46.515 21 18 1 6 1 2 4 8 16 32 226 465 42 | 35 63.385 11.981 16 21 1 6 1 2 4 8 16 32 66 411 43 | 36 47.192 -5.475 23 10 1 6 1 2 4 8 16 32 173 357 44 | 37 -16.315 -11.267 21 23 1 6 1 2 4 8 16 32 283 573 45 | 38 78.900 17.651 15 23 1 6 1 2 4 8 16 32 261 489 46 | 39 79.822 22.272 7 11 1 6 1 2 4 8 16 32 254 446 47 | 40 12.878 16.919 20 1 1 6 1 2 4 8 16 32 179 538 48 | 41 -67.981 -3.754 6 23 1 6 1 2 4 8 16 32 253 440 49 | 42 9.198 -18.597 16 16 1 6 1 2 4 8 16 32 165 524 50 | 43 -35.950 -19.141 10 10 1 6 1 2 4 8 16 32 72 425 51 | 44 28.766 45.276 7 12 1 6 1 2 4 8 16 32 142 452 52 | 45 11.469 68.231 20 12 1 6 1 2 4 8 16 32 277 564 53 | 46 -22.760 45.496 9 3 1 6 1 2 4 8 16 32 203 439 54 | 47 -65.674 -23.120 12 22 1 6 1 2 4 8 16 32 97 402 55 | 48 7.239 1.599 10 21 1 6 1 2 4 8 16 32 96 340 56 | 49 -29.785 -11.285 19 13 1 6 1 2 4 8 16 32 155 513 57 | 50 -89.050 16.211 6 15 1 6 1 2 4 8 16 32 96 340 58 | 51 -46.887 -3.363 14 13 1 6 1 2 4 8 16 32 85 280 59 | 52 -14.972 30.621 23 20 1 6 1 2 4 8 16 32 212 448 60 | 53 -17.035 49.774 8 10 1 6 1 2 4 8 16 32 197 488 61 | 54 31.635 53.619 10 25 1 6 1 2 4 8 16 32 101 359 62 | 55 -3.577 13.342 14 7 1 6 1 2 4 8 16 32 104 337 63 | 56 33.008 58.960 3 15 1 6 1 2 4 8 16 32 243 511 64 | 57 -92.950 63.263 25 2 1 6 1 2 4 8 16 32 129 329 65 | 58 -9.137 -22.931 21 23 1 6 1 2 4 8 16 32 267 563 66 | 59 -39.960 6.195 5 5 1 6 1 2 4 8 16 32 81 325 67 | 60 28.430 -19.214 2 25 1 6 1 2 4 8 16 32 264 512 68 | 61 -28.540 -3.485 3 9 1 6 1 2 4 8 16 32 243 563 69 | 62 31.415 36.859 21 2 1 6 1 2 4 8 16 32 256 508 70 | 63 -49.426 60.602 1 24 1 6 1 2 4 8 16 32 155 423 71 | 64 -72.827 -27.765 25 13 1 6 1 2 4 8 16 32 298 478 72 | 65 60.083 -45.905 21 20 1 6 1 2 4 8 16 32 234 577 73 | 66 10.870 -3.900 21 13 1 6 1 2 4 8 16 32 218 552 74 | 67 25.122 7.672 25 15 1 6 1 2 4 8 16 32 164 417 75 | 68 -46.997 -17.474 14 4 1 6 1 2 4 8 16 32 276 592 76 | 69 16.058 33.020 20 20 1 6 1 2 4 8 16 32 291 642 77 | 70 25.409 -11.700 14 8 1 6 1 2 4 8 16 32 69 277 78 | 71 68.323 -5.145 11 19 1 6 1 2 4 8 16 32 181 429 79 | 72 -13.104 62.158 25 20 1 6 1 2 4 8 16 32 81 286 80 | 73 42.395 -8.344 0 0 0 0 0 1000 81 | 74 -42.175 -14.554 0 0 0 0 0 1000 82 | 75 16.034 40.726 0 0 0 0 0 1000 83 | 76 -14.639 29.633 0 0 0 0 0 1000 84 | 77 16.049 -3.934 0 0 0 0 0 1000 85 | 78 46.112 12.430 0 0 0 0 0 1000 86 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr02.txt: -------------------------------------------------------------------------------- 1 | 6 3 96 4 2 | 480 195 3 | 480 195 4 | 480 195 5 | 480 195 6 | 1 33.588 30.750 12 4 1 4 1 2 4 8 410 539 7 | 2 48.828 65.314 1 12 1 4 1 2 4 8 125 278 8 | 3 86.176 59.344 1 3 1 4 1 2 4 8 256 378 9 | 4 39.270 -33.057 15 15 1 4 1 2 4 8 382 504 10 | 5 -23.370 86.853 10 13 1 4 1 2 4 8 422 539 11 | 6 48.132 95.593 12 20 1 4 1 2 4 8 336 444 12 | 7 -16.357 93.311 15 21 1 4 1 2 4 8 257 387 13 | 8 -57.703 -65.601 16 4 1 4 1 2 4 8 81 231 14 | 9 7.147 32.684 18 25 1 4 1 2 4 8 314 439 15 | 10 42.950 68.701 9 21 1 4 1 2 4 8 345 505 16 | 11 37.085 -2.112 5 7 1 4 1 2 4 8 143 253 17 | 12 77.759 55.817 19 3 1 4 1 2 4 8 205 372 18 | 13 -17.462 -56.567 11 9 1 4 1 2 4 8 417 591 19 | 14 58.575 59.888 12 5 1 4 1 2 4 8 402 559 20 | 15 57.776 15.344 13 20 1 4 1 2 4 8 114 285 21 | 16 -22.327 36.072 8 21 1 4 1 2 4 8 376 549 22 | 17 -7.080 30.493 14 19 1 4 1 2 4 8 298 434 23 | 18 55.658 60.425 9 2 1 4 1 2 4 8 445 572 24 | 19 -14.307 11.456 2 16 1 4 1 2 4 8 213 347 25 | 20 -29.724 24.268 25 18 1 4 1 2 4 8 206 358 26 | 21 43.219 0.739 6 24 1 4 1 2 4 8 161 257 27 | 22 45.184 35.474 16 24 1 4 1 2 4 8 378 522 28 | 23 64.484 2.240 18 8 1 4 1 2 4 8 228 368 29 | 24 55.078 72.241 4 16 1 4 1 2 4 8 402 535 30 | 25 16.925 15.741 17 19 1 4 1 2 4 8 103 228 31 | 26 45.038 -3.723 4 12 1 4 1 2 4 8 314 446 32 | 27 -76.782 5.939 22 9 1 4 1 2 4 8 387 508 33 | 28 36.169 0.256 12 3 1 4 1 2 4 8 447 555 34 | 29 29.218 8.936 13 15 1 4 1 2 4 8 143 310 35 | 30 65.057 5.225 5 4 1 4 1 2 4 8 332 498 36 | 31 42.175 -22.284 24 24 1 4 1 2 4 8 316 425 37 | 32 25.574 31.726 12 22 1 4 1 2 4 8 444 606 38 | 33 31.561 37.262 16 14 1 4 1 2 4 8 216 360 39 | 34 66.498 -54.169 8 11 1 4 1 2 4 8 188 352 40 | 35 46.576 -17.938 10 20 1 4 1 2 4 8 476 615 41 | 36 65.063 40.875 15 5 1 4 1 2 4 8 375 473 42 | 37 -2.716 24.768 21 11 1 4 1 2 4 8 100 197 43 | 38 -40.002 3.870 5 12 1 4 1 2 4 8 234 396 44 | 39 -73.505 57.043 9 7 1 4 1 2 4 8 171 343 45 | 40 81.146 -25.714 5 1 1 4 1 2 4 8 152 326 46 | 41 12.006 -7.965 9 23 1 4 1 2 4 8 140 319 47 | 42 42.761 38.092 20 13 1 4 1 2 4 8 178 341 48 | 43 3.857 -23.181 14 4 1 4 1 2 4 8 136 294 49 | 44 -7.367 24.390 24 23 1 4 1 2 4 8 455 622 50 | 45 35.944 -11.835 23 17 1 4 1 2 4 8 231 369 51 | 46 52.075 9.692 12 13 1 4 1 2 4 8 322 446 52 | 47 30.725 30.701 13 9 1 4 1 2 4 8 450 566 53 | 48 41.223 77.924 19 10 1 4 1 2 4 8 162 334 54 | 49 68.884 -40.546 21 22 1 4 1 2 4 8 439 540 55 | 50 76.312 86.670 5 21 1 4 1 2 4 8 216 357 56 | 51 63.934 78.540 9 13 1 4 1 2 4 8 70 243 57 | 52 29.150 -9.961 23 25 1 4 1 2 4 8 436 537 58 | 53 85.522 39.954 8 8 1 4 1 2 4 8 300 425 59 | 54 31.775 3.870 5 20 1 4 1 2 4 8 210 347 60 | 55 -20.544 19.086 8 3 1 4 1 2 4 8 104 279 61 | 56 55.353 43.817 10 4 1 4 1 2 4 8 370 467 62 | 57 35.406 10.278 9 13 1 4 1 2 4 8 169 316 63 | 58 25.464 -0.287 21 18 1 4 1 2 4 8 424 548 64 | 59 12.396 0.244 12 1 1 4 1 2 4 8 300 449 65 | 60 18.359 20.917 8 10 1 4 1 2 4 8 137 233 66 | 61 27.960 -15.039 4 16 1 4 1 2 4 8 422 564 67 | 62 -3.192 19.879 14 9 1 4 1 2 4 8 95 230 68 | 63 9.332 -41.351 19 13 1 4 1 2 4 8 107 272 69 | 64 -20.581 38.177 23 23 1 4 1 2 4 8 299 433 70 | 65 27.820 28.729 4 14 1 4 1 2 4 8 180 323 71 | 66 59.027 42.310 9 5 1 4 1 2 4 8 222 322 72 | 67 56.311 58.734 1 1 1 4 1 2 4 8 109 253 73 | 68 -51.654 -0.342 21 6 1 4 1 2 4 8 237 392 74 | 69 2.576 32.721 20 20 1 4 1 2 4 8 178 283 75 | 70 15.131 -3.046 24 10 1 4 1 2 4 8 285 405 76 | 71 66.595 34.937 22 19 1 4 1 2 4 8 457 606 77 | 72 19.476 20.142 25 24 1 4 1 2 4 8 300 416 78 | 73 -1.172 20.648 19 3 1 4 1 2 4 8 129 299 79 | 74 -21.204 13.660 14 5 1 4 1 2 4 8 372 511 80 | 75 98.846 1.257 11 13 1 4 1 2 4 8 95 260 81 | 76 82.593 72.003 22 5 1 4 1 2 4 8 122 251 82 | 77 30.017 -30.896 25 4 1 4 1 2 4 8 233 387 83 | 78 33.228 41.663 5 12 1 4 1 2 4 8 306 411 84 | 79 39.490 -8.539 10 11 1 4 1 2 4 8 129 234 85 | 80 54.498 70.813 4 20 1 4 1 2 4 8 248 346 86 | 81 3.058 10.248 4 13 1 4 1 2 4 8 106 251 87 | 82 51.831 -16.870 6 1 1 4 1 2 4 8 410 570 88 | 83 76.416 11.346 22 3 1 4 1 2 4 8 432 539 89 | 84 5.243 0.238 17 2 1 4 1 2 4 8 350 455 90 | 85 50.592 34.247 22 23 1 4 1 2 4 8 190 339 91 | 86 -5.231 -2.081 3 8 1 4 1 2 4 8 110 257 92 | 87 24.194 25.836 25 25 1 4 1 2 4 8 383 531 93 | 88 33.630 37.585 22 18 1 4 1 2 4 8 324 442 94 | 89 31.445 -7.001 23 4 1 4 1 2 4 8 158 285 95 | 90 30.707 -28.168 16 5 1 4 1 2 4 8 272 413 96 | 91 49.860 1.038 13 21 1 4 1 2 4 8 404 554 97 | 92 -35.767 14.142 23 23 1 4 1 2 4 8 82 254 98 | 93 -29.309 4.889 1 19 1 4 1 2 4 8 216 370 99 | 94 19.049 41.974 11 2 1 4 1 2 4 8 442 553 100 | 95 27.600 13.934 19 21 1 4 1 2 4 8 85 192 101 | 96 52.832 50.684 21 10 1 4 1 2 4 8 124 269 102 | 97 6.229 10.590 0 0 0 0 0 1000 103 | 98 32.663 44.730 0 0 0 0 0 1000 104 | 99 48.807 48.792 0 0 0 0 0 1000 105 | 100 33.179 -4.968 0 0 0 0 0 1000 106 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr12.txt: -------------------------------------------------------------------------------- 1 | 6 2 96 4 2 | 480 195 3 | 480 195 4 | 480 195 5 | 480 195 6 | 1 33.588 30.750 12 4 1 4 1 2 4 8 157 417 7 | 2 48.828 65.314 1 12 1 4 1 2 4 8 190 405 8 | 3 86.176 59.344 1 3 1 4 1 2 4 8 223 555 9 | 4 39.270 -33.057 15 15 1 4 1 2 4 8 113 306 10 | 5 -23.370 86.853 10 13 1 4 1 2 4 8 296 481 11 | 6 48.132 95.593 12 20 1 4 1 2 4 8 72 368 12 | 7 -16.357 93.311 15 21 1 4 1 2 4 8 157 457 13 | 8 -57.703 -65.601 16 4 1 4 1 2 4 8 279 560 14 | 9 7.147 32.684 18 25 1 4 1 2 4 8 216 429 15 | 10 42.950 68.701 9 21 1 4 1 2 4 8 232 544 16 | 11 37.085 -2.112 5 7 1 4 1 2 4 8 282 626 17 | 12 77.759 55.817 19 3 1 4 1 2 4 8 219 569 18 | 13 -17.462 -56.567 11 9 1 4 1 2 4 8 67 423 19 | 14 58.575 59.888 12 5 1 4 1 2 4 8 129 430 20 | 15 57.776 15.344 13 20 1 4 1 2 4 8 218 456 21 | 16 -22.327 36.072 8 21 1 4 1 2 4 8 299 575 22 | 17 -7.080 30.493 14 19 1 4 1 2 4 8 144 340 23 | 18 55.658 60.425 9 2 1 4 1 2 4 8 127 329 24 | 19 -14.307 11.456 2 16 1 4 1 2 4 8 66 412 25 | 20 -29.724 24.268 25 18 1 4 1 2 4 8 149 444 26 | 21 43.219 0.739 6 24 1 4 1 2 4 8 222 445 27 | 22 45.184 35.474 16 24 1 4 1 2 4 8 187 386 28 | 23 64.484 2.240 18 8 1 4 1 2 4 8 77 280 29 | 24 55.078 72.241 4 16 1 4 1 2 4 8 225 514 30 | 25 16.925 15.741 17 19 1 4 1 2 4 8 299 649 31 | 26 45.038 -3.723 4 12 1 4 1 2 4 8 219 442 32 | 27 -76.782 5.939 22 9 1 4 1 2 4 8 64 278 33 | 28 36.169 0.256 12 3 1 4 1 2 4 8 221 455 34 | 29 29.218 8.936 13 15 1 4 1 2 4 8 102 414 35 | 30 65.057 5.225 5 4 1 4 1 2 4 8 146 397 36 | 31 42.175 -22.284 24 24 1 4 1 2 4 8 216 410 37 | 32 25.574 31.726 12 22 1 4 1 2 4 8 79 424 38 | 33 31.561 37.262 16 14 1 4 1 2 4 8 254 550 39 | 34 66.498 -54.169 8 11 1 4 1 2 4 8 198 504 40 | 35 46.576 -17.938 10 20 1 4 1 2 4 8 207 517 41 | 36 65.063 40.875 15 5 1 4 1 2 4 8 269 480 42 | 37 -2.716 24.768 21 11 1 4 1 2 4 8 282 496 43 | 38 -40.002 3.870 5 12 1 4 1 2 4 8 196 541 44 | 39 -73.505 57.043 9 7 1 4 1 2 4 8 190 475 45 | 40 81.146 -25.714 5 1 1 4 1 2 4 8 101 442 46 | 41 12.006 -7.965 9 23 1 4 1 2 4 8 201 490 47 | 42 42.761 38.092 20 13 1 4 1 2 4 8 264 486 48 | 43 3.857 -23.181 14 4 1 4 1 2 4 8 84 383 49 | 44 -7.367 24.390 24 23 1 4 1 2 4 8 95 446 50 | 45 35.944 -11.835 23 17 1 4 1 2 4 8 145 423 51 | 46 52.075 9.692 12 13 1 4 1 2 4 8 61 305 52 | 47 30.725 30.701 13 9 1 4 1 2 4 8 271 601 53 | 48 41.223 77.924 19 10 1 4 1 2 4 8 243 530 54 | 49 68.884 -40.546 21 22 1 4 1 2 4 8 207 407 55 | 50 76.312 86.670 5 21 1 4 1 2 4 8 144 336 56 | 51 63.934 78.540 9 13 1 4 1 2 4 8 104 392 57 | 52 29.150 -9.961 23 25 1 4 1 2 4 8 155 364 58 | 53 85.522 39.954 8 8 1 4 1 2 4 8 252 484 59 | 54 31.775 3.870 5 20 1 4 1 2 4 8 180 373 60 | 55 -20.544 19.086 8 3 1 4 1 2 4 8 222 434 61 | 56 55.353 43.817 10 4 1 4 1 2 4 8 138 339 62 | 57 35.406 10.278 9 13 1 4 1 2 4 8 179 487 63 | 58 25.464 -0.287 21 18 1 4 1 2 4 8 254 487 64 | 59 12.396 0.244 12 1 1 4 1 2 4 8 275 633 65 | 60 18.359 20.917 8 10 1 4 1 2 4 8 89 367 66 | 61 27.960 -15.039 4 16 1 4 1 2 4 8 150 485 67 | 62 -3.192 19.879 14 9 1 4 1 2 4 8 224 490 68 | 63 9.332 -41.351 19 13 1 4 1 2 4 8 224 496 69 | 64 -20.581 38.177 23 23 1 4 1 2 4 8 270 619 70 | 65 27.820 28.729 4 14 1 4 1 2 4 8 76 361 71 | 66 59.027 42.310 9 5 1 4 1 2 4 8 238 460 72 | 67 56.311 58.734 1 1 1 4 1 2 4 8 166 437 73 | 68 -51.654 -0.342 21 6 1 4 1 2 4 8 137 393 74 | 69 2.576 32.721 20 20 1 4 1 2 4 8 264 448 75 | 70 15.131 -3.046 24 10 1 4 1 2 4 8 276 530 76 | 71 66.595 34.937 22 19 1 4 1 2 4 8 129 422 77 | 72 19.476 20.142 25 24 1 4 1 2 4 8 262 498 78 | 73 -1.172 20.648 19 3 1 4 1 2 4 8 121 464 79 | 74 -21.204 13.660 14 5 1 4 1 2 4 8 129 460 80 | 75 98.846 1.257 11 13 1 4 1 2 4 8 82 337 81 | 76 82.593 72.003 22 5 1 4 1 2 4 8 141 335 82 | 77 30.017 -30.896 25 4 1 4 1 2 4 8 185 405 83 | 78 33.228 41.663 5 12 1 4 1 2 4 8 75 347 84 | 79 39.490 -8.539 10 11 1 4 1 2 4 8 149 442 85 | 80 54.498 70.813 4 20 1 4 1 2 4 8 290 586 86 | 81 3.058 10.248 4 13 1 4 1 2 4 8 287 512 87 | 82 51.831 -16.870 6 1 1 4 1 2 4 8 85 273 88 | 83 76.416 11.346 22 3 1 4 1 2 4 8 74 431 89 | 84 5.243 0.238 17 2 1 4 1 2 4 8 240 443 90 | 85 50.592 34.247 22 23 1 4 1 2 4 8 82 439 91 | 86 -5.231 -2.081 3 8 1 4 1 2 4 8 192 481 92 | 87 24.194 25.836 25 25 1 4 1 2 4 8 103 330 93 | 88 33.630 37.585 22 18 1 4 1 2 4 8 90 425 94 | 89 31.445 -7.001 23 4 1 4 1 2 4 8 270 617 95 | 90 30.707 -28.168 16 5 1 4 1 2 4 8 118 324 96 | 91 49.860 1.038 13 21 1 4 1 2 4 8 144 467 97 | 92 -35.767 14.142 23 23 1 4 1 2 4 8 120 461 98 | 93 -29.309 4.889 1 19 1 4 1 2 4 8 189 417 99 | 94 19.049 41.974 11 2 1 4 1 2 4 8 71 262 100 | 95 27.600 13.934 19 21 1 4 1 2 4 8 111 347 101 | 96 52.832 50.684 21 10 1 4 1 2 4 8 128 448 102 | 97 6.229 10.590 0 0 0 0 0 1000 103 | 98 32.663 44.730 0 0 0 0 0 1000 104 | 99 48.807 48.792 0 0 0 0 0 1000 105 | 100 33.179 -4.968 0 0 0 0 0 1000 106 | -------------------------------------------------------------------------------- /shakespeare.py: -------------------------------------------------------------------------------- 1 | import random, string, copy 2 | 3 | 4 | class DNA: 5 | 6 | def __init__(self, target): 7 | self.genes = [] # cambiar esto a una lista dado que los strings son inmutables en python 8 | self.fitness = 0 9 | self.target = target 10 | letters = string.ascii_lowercase + ' ' 11 | for i in range(len(self.target)): 12 | self.genes.append(random.choice(letters)) 13 | 14 | def calculate_fitness(self): 15 | score = 0 16 | for i in range(len(self.target)): 17 | if self.genes[i] == self.target[i]: 18 | score += 1 19 | self.fitness = round((score / len(self.target)), 4) #this is a linear approach 20 | # self.fitness = round ( pow(2, score) / 100, 4) 21 | 22 | 23 | def crossover(self, partner): 24 | child = DNA(self.target) 25 | new_genes = [] 26 | midpoint = int(random.choice(range(len(partner.genes)))) 27 | for i in range(len(child.genes)): 28 | if i > midpoint: 29 | new_genes.append(self.genes[i]) 30 | else: 31 | new_genes.append(partner.genes[i]) 32 | child.genes = new_genes 33 | return child 34 | 35 | def mutate(self, mutation_rate): 36 | letters = string.ascii_lowercase + ' ' 37 | for i in range(len(self.genes)): 38 | random_n = round(random.randint(1, 100)/100, 2) 39 | if (random_n < mutation_rate): 40 | self.genes[i] = random.choice(letters) 41 | 42 | def getPhenotype(self): 43 | return ''.join(self.genes) 44 | 45 | 46 | 47 | 48 | 49 | def getNeighboors(population, index): 50 | newPopulation = population.copy() 51 | newPopulation.pop(index) 52 | return newPopulation 53 | 54 | def getMinFitness(population): 55 | minFitness = 2 56 | for i in population: 57 | if population[i].fitness < minFitness: 58 | minFitness = population[i].fitness 59 | return minFitness 60 | 61 | 62 | def intelligent_mutation(state): 63 | letters = string.ascii_lowercase + ' ' 64 | newNode = copy.deepcopy(state) 65 | for i in range(len(state.genes)): 66 | if state.genes[i] != state.target[i]: 67 | newNode.genes[i] = random.choice(letters) 68 | newNode.calculate_fitness() 69 | return newNode 70 | 71 | def climbing_hill(population, index): 72 | currentNode = population[index] 73 | while currentNode.fitness < 0.99: 74 | newNode = intelligent_mutation(currentNode) 75 | print(currentNode.fitness, newNode.fitness) 76 | if newNode.fitness > currentNode.fitness: 77 | currentNode = newNode 78 | return currentNode 79 | 80 | def mating_pool(population): 81 | pool = [] 82 | for i in range(len(population)): 83 | n = int(population[i].fitness * 100) 84 | for j in range(n): 85 | pool.append(population[i]) 86 | return pool 87 | 88 | def check_phrase(population, phrase): 89 | 90 | for i in range(len(population)): 91 | if population[i].getPhenotype() == phrase: 92 | return True 93 | return False 94 | 95 | def reproduction(population, pool, mutation_rate): 96 | offspring = [] 97 | for i in range(len(population)): 98 | parent_a = random.choice(pool) 99 | parent_b = random.choice(pool) 100 | child = parent_a.crossover(parent_b) 101 | child.mutate(mutation_rate) 102 | # child.fitness 103 | offspring.append(child) 104 | return offspring 105 | 106 | population = [] 107 | mutation_rate = 0.02 108 | population_n = 200 109 | phrase = 'the cat ate my source code' 110 | generations = 130 111 | n = 0 112 | max_fitness = 0 113 | best_gen = DNA(phrase) 114 | best_gen.calculate_fitness() 115 | #initialize population 116 | for i in range(population_n): 117 | dna = DNA(phrase) 118 | dna.calculate_fitness() 119 | population.append(dna) 120 | # random_index = random.randint(0, len(population) - 1) 121 | # newAgent = climbing_hill(population, random_index) 122 | # print(newAgent.fitness, newAgent.getPhenotype()) 123 | # # print (climbing_hill(population, random.choice(population) )) 124 | 125 | #check if in the initial population there is at least one that is a exact match against the phrase 126 | is_target = check_phrase(population, phrase) 127 | 128 | while is_target == False: 129 | #calculate fitness 130 | for i in range(len(population)): 131 | population[i].calculate_fitness() 132 | 133 | # build a mating pool 134 | pool = mating_pool(population) #selection 135 | population = reproduction(population, pool, mutation_rate) 136 | is_target = check_phrase(population, phrase) 137 | n += 1 138 | 139 | for i in range(len(population)): 140 | population[i].calculate_fitness() 141 | if population[i].fitness > max_fitness: 142 | best_gen = population[i] 143 | max_fitness = best_gen.fitness 144 | print('\n') 145 | print(best_gen.genes, best_gen.fitness, n) 146 | # for i in range(len(population)): 147 | # print (population[i].genes, population[i].fitness) 148 | 149 | # parent_a = random.choice(pool) 150 | # parent_b = random.choice(pool) 151 | # print ('\n') 152 | # print(parent_a.genes, parent_a.fitness) 153 | # print(parent_b.genes, parent_b.fitness) 154 | # child = parent_a.crossover(parent_b) 155 | # print (child.genes, child.fitness) 156 | # child.mutate(mutation_rate) 157 | # print (child.genes, child.fitness) 158 | 159 | # print(population) 160 | 161 | # create a population of n individuals 162 | # def createPopulation(n): 163 | # population = [] 164 | # for individual in range(n): 165 | # lowercase_letters = string.ascii_lowercase 166 | # random_string = random.choice(lowercase_letters) + random.choice(lowercase_letters) + random.choice(lowercase_letters) 167 | # population.append(random_string) 168 | # return population 169 | 170 | 171 | # print(createPopulation(50)) 172 | 173 | -------------------------------------------------------------------------------- /basic/basicfunctions.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | 3 | import os 4 | import json 5 | import operator 6 | import common 7 | import random 8 | 9 | def sortbyPrice(bidList): 10 | bidList.sort(key= operator.itemgetter(1)) 11 | return bidList 12 | 13 | def getcapacities(stringline): 14 | vehicle_capacity = [] 15 | for i in range(1, len(stringline)): 16 | vehicle_capacity.append(int(stringline[i])) 17 | return vehicle_capacity 18 | 19 | 20 | def getLots(instanceLots): 21 | lenLots = len(instanceLots) 22 | lots = {} 23 | for i in range (0, lenLots): 24 | product_id = instanceLots[i]['semilla_id'] 25 | demand = instanceLots[i]['cantidad'] 26 | lots['%i' %int(product_id)] = demand 27 | return lots 28 | 29 | def coveredDemand(instanceLots, instanceBids, bidsroute, delivery): 30 | lots = instanceLots 31 | for routeid in bidsroute: 32 | mybids = bidsroute[routeid] 33 | for bidid, price, amount in mybids: 34 | lotid = instanceBids[str(bidid)]['lot_id'] 35 | lots['lots'][lotid]['demand']-=amount 36 | for bidID in delivery: 37 | lotid = instanceBids[bidID]['lot_id'] 38 | available = delivery[bidID][1] 39 | lots['lots'][lotid]['demand']-=available 40 | return lots 41 | 42 | 43 | #Función para evaluar si queda demanda restante 44 | def demandLeft(lots): 45 | jsonData = {} 46 | jsonData['demand_left']=[] 47 | lenlots = len(lots) 48 | for aux in range(0,lenlots): 49 | productid = lots[aux]['product_id'] 50 | missing = lots[aux]['demand'] 51 | if(lots[aux]['demand']>0): 52 | jsonData['demand_left'].append({ 53 | 'semilla_id': productid, 54 | 'missing': missing 55 | }) 56 | return jsonData 57 | 58 | 59 | #Función para generar lotes artificiales de ofertas 60 | def bidsGenerator(instanceProducts, instanceLots, nBids, vendors): 61 | maxPrice = 350 62 | maxAvailable = 500 63 | productsID = instanceProducts['product_base'].keys() 64 | lenLots = len(instanceLots['lots']) 65 | jsonData = {} 66 | ct = 0 67 | for i in range(0,nBids): 68 | lotID = 0 69 | randomProduct = random.choice(productsID) 70 | randomVendor = random.randint(1, vendors) 71 | randomPrice = random.randint(50, maxPrice) 72 | randomAvailable = random.randint(100, maxAvailable) 73 | for auxLot in range(0, lenLots): 74 | productLotID = instanceLots['lots'][auxLot]['product_id'] 75 | if (randomProduct == productLotID): 76 | lotID = auxLot 77 | jsonData['%s' %ct]={ 78 | 'vendor_id': str(randomVendor), 79 | 'product_id': randomProduct, 80 | 'lot_id': lotID, 81 | 'price': randomPrice, 82 | 'available': randomAvailable, 83 | 'delivery': random.randint(0,1) 84 | } 85 | ct+=1 86 | return jsonData 87 | 88 | 89 | #Función para generar lotes artificiales de demanda 90 | def lotsGenerator(instanceProducts, nLots): 91 | productsID = instanceProducts['product_base'].keys() 92 | MaxDemand = 500 93 | jsonData = {} 94 | jsonData['lots'] = [] 95 | for i in range(0, nLots): 96 | randomProduct = random.choice(productsID) 97 | jsonData['lots'].append({ 98 | 'product_id': randomProduct, 99 | 'demand': random.randint(0,MaxDemand) 100 | }) 101 | return jsonData 102 | 103 | #Obtener bids asociados a un vendorID 104 | def getBids4ind(vendorID, instanceBids): 105 | listofBids = [] 106 | lenBids = len(instanceBids) 107 | for bidid in range(0,lenBids): 108 | user_id = instanceBids[bidid]['user_id'] 109 | price = instanceBids[bidid]['precio'] 110 | if (str(user_id) == vendorID): 111 | listofBids.append([bidid,price]) 112 | return listofBids 113 | 114 | 115 | def generatedelivery(delivery, instanceBids): 116 | jsonData = {} 117 | jsonData['delivery'] = [] 118 | for key in delivery: 119 | vendor_id = instanceBids[int(key)]['user_id'] 120 | jsonData['delivery'].append({ 121 | 'bidid': key, 122 | 'vendorid': vendor_id, 123 | 'price': delivery[key][0], 124 | 'amount': delivery[key][1] 125 | }) 126 | return jsonData 127 | 128 | #Generar bids para la ruta 129 | def GenerateRouteData(routes, instanceBids, bidsroute): 130 | jsonData = {} 131 | jsonData['routes']=[] 132 | routeid = 0 133 | for route in routes: 134 | routestr = route.replace(" ", "") 135 | mybids = bidsroute[str(routeid)] 136 | tobuy = [] 137 | for bidid, price, amount in mybids: 138 | vendorid = instanceBids[bidid]['user_id'] 139 | semillaid = instanceBids[bidid]['semilla_id'] 140 | tobuy.append({ 141 | 'amount': amount, 142 | 'vendorid': vendorid, 143 | 'semilla_id': semillaid, 144 | 'price': price 145 | }) 146 | jsonData['routes'].append({ 147 | 'route':routestr, 148 | 'to_buy':tobuy, 149 | }) 150 | routeid+=1 151 | return jsonData 152 | 153 | #Escribir al archivo Json 154 | def writeFileJson(jsonData, instName , DataDir): 155 | jsonFilename = '%s.json' % instName 156 | jsonFile = os.path.join(DataDir, jsonFilename) 157 | print 'Writing %s to: %s' %(jsonFilename, DataDir) 158 | with open(jsonFile, 'w') as f: 159 | json.dump(jsonData, f, sort_keys=True) 160 | #json.dump(jsonData, f, indent=4, separators=(',', ': '), sort_keys=True) 161 | 162 | 163 | def getFile(instName, dataDir): 164 | myJson = os.path.join(dataDir,'%s.json' %instName) 165 | with open(myJson) as f: 166 | instance = json.load(f) 167 | return instance 168 | 169 | 170 | def generateCustomerRouteData(myroute, instancecustomers): 171 | jsonData = {} 172 | jsonData['routes'] = [] 173 | for subroute in myroute: 174 | product_deliver = [] 175 | subroute = subroute.replace(" ","") 176 | for customerID in subroute: 177 | if customerID !='0' and customerID!='-' and customerID: 178 | products = instancecustomers["customer_%s"%customerID]["products_deliver"] 179 | customerid = instancecustomers["customer_%s"%customerID]["id"] 180 | for productid, amount in products: 181 | product_deliver.append({ 182 | 'product_id': productid, 183 | 'amount': amount, 184 | 'customer_id':customerid 185 | }) 186 | jsonData['routes'].append({ 187 | 'route': subroute, 188 | 'deliver': product_deliver 189 | }) 190 | return jsonData 191 | 192 | 193 | def getvendors_idsfromjson(instance): 194 | vendorsid = [] 195 | for key in instance: 196 | if "customer_" in key: 197 | fullstring = key.split("_") 198 | id = fullstring[1] 199 | vendorsid.append(int(id)) 200 | return vendorsid 201 | 202 | 203 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr03.txt: -------------------------------------------------------------------------------- 1 | 6 4 144 4 2 | 460 190 3 | 460 190 4 | 460 190 5 | 460 190 6 | 1 -55.280 -24.371 25 9 1 4 1 2 4 8 410 539 7 | 2 -48.297 53.314 13 22 1 4 1 2 4 8 125 278 8 | 3 -49.072 -38.489 20 10 1 4 1 2 4 8 256 378 9 | 4 25.311 -18.561 16 24 1 4 1 2 4 8 382 504 10 | 5 -24.469 -3.815 4 25 1 4 1 2 4 8 422 539 11 | 6 24.591 -17.896 2 6 1 4 1 2 4 8 336 444 12 | 7 -10.419 60.364 7 22 1 4 1 2 4 8 257 387 13 | 8 38.177 -35.175 15 1 1 4 1 2 4 8 81 231 14 | 9 -68.280 93.073 10 19 1 4 1 2 4 8 314 439 15 | 10 -30.792 -57.336 4 19 1 4 1 2 4 8 345 505 16 | 11 -37.061 -12.122 19 5 1 4 1 2 4 8 143 253 17 | 12 -15.741 -47.638 10 9 1 4 1 2 4 8 205 372 18 | 13 -63.379 -22.919 15 8 1 4 1 2 4 8 417 591 19 | 14 -43.109 -43.439 4 14 1 4 1 2 4 8 402 559 20 | 15 -25.623 13.599 17 4 1 4 1 2 4 8 114 285 21 | 16 -2.625 16.632 4 7 1 4 1 2 4 8 376 549 22 | 17 -41.577 -28.497 3 10 1 4 1 2 4 8 298 434 23 | 18 2.081 12.885 9 22 1 4 1 2 4 8 445 572 24 | 19 3.925 -47.845 25 17 1 4 1 2 4 8 213 347 25 | 20 -83.295 26.324 14 10 1 4 1 2 4 8 206 358 26 | 21 -6.458 26.355 23 8 1 4 1 2 4 8 161 257 27 | 22 3.290 6.732 7 14 1 4 1 2 4 8 378 522 28 | 23 -34.869 30.426 7 12 1 4 1 2 4 8 228 368 29 | 24 15.546 -36.273 1 17 1 4 1 2 4 8 402 535 30 | 25 12.842 5.127 10 19 1 4 1 2 4 8 103 228 31 | 26 -48.450 -24.426 15 25 1 4 1 2 4 8 314 446 32 | 27 -88.538 -10.461 1 25 1 4 1 2 4 8 387 508 33 | 28 -29.773 0.995 2 6 1 4 1 2 4 8 447 555 34 | 29 9.827 38.416 9 21 1 4 1 2 4 8 143 310 35 | 30 1.410 92.938 10 3 1 4 1 2 4 8 332 498 36 | 31 -79.303 15.381 2 19 1 4 1 2 4 8 316 425 37 | 32 -30.652 40.063 23 16 1 4 1 2 4 8 444 606 38 | 33 18.927 21.637 15 21 1 4 1 2 4 8 216 360 39 | 34 45.087 -59.906 13 17 1 4 1 2 4 8 188 352 40 | 35 -34.949 -3.815 16 19 1 4 1 2 4 8 476 615 41 | 36 81.201 -74.744 21 6 1 4 1 2 4 8 375 473 42 | 37 15.594 -28.455 1 10 1 4 1 2 4 8 100 197 43 | 38 -72.192 29.547 18 22 1 4 1 2 4 8 234 396 44 | 39 -42.914 -22.675 17 17 1 4 1 2 4 8 171 343 45 | 40 -76.392 -57.489 13 16 1 4 1 2 4 8 152 326 46 | 41 -28.540 12.073 5 5 1 4 1 2 4 8 140 319 47 | 42 -61.389 26.526 23 1 1 4 1 2 4 8 178 341 48 | 43 -8.472 12.616 20 21 1 4 1 2 4 8 136 294 49 | 44 -61.475 33.392 1 20 1 4 1 2 4 8 455 622 50 | 45 -34.674 -27.588 2 6 1 4 1 2 4 8 231 369 51 | 46 10.315 -12.518 15 1 1 4 1 2 4 8 322 446 52 | 47 -83.014 77.002 20 11 1 4 1 2 4 8 450 566 53 | 48 -40.417 49.988 9 22 1 4 1 2 4 8 162 334 54 | 49 83.832 33.905 13 25 1 4 1 2 4 8 439 540 55 | 50 -20.563 -75.830 4 18 1 4 1 2 4 8 216 357 56 | 51 15.442 -18.719 15 9 1 4 1 2 4 8 70 243 57 | 52 -59.937 -65.802 5 23 1 4 1 2 4 8 436 537 58 | 53 5.151 47.815 8 17 1 4 1 2 4 8 300 425 59 | 54 46.008 14.990 19 4 1 4 1 2 4 8 210 347 60 | 55 82.977 -1.660 16 1 1 4 1 2 4 8 104 279 61 | 56 39.893 -38.916 11 14 1 4 1 2 4 8 370 467 62 | 57 90.839 -83.539 23 11 1 4 1 2 4 8 169 316 63 | 58 7.068 0.067 14 7 1 4 1 2 4 8 424 548 64 | 59 18.958 40.088 24 21 1 4 1 2 4 8 300 449 65 | 60 -80.487 58.209 5 3 1 4 1 2 4 8 137 233 66 | 61 64.459 -62.946 5 18 1 4 1 2 4 8 422 564 67 | 62 -43.616 -26.117 23 5 1 4 1 2 4 8 95 230 68 | 63 -18.408 10.303 3 6 1 4 1 2 4 8 107 272 69 | 64 -43.921 63.251 10 22 1 4 1 2 4 8 299 433 70 | 65 11.353 26.221 23 13 1 4 1 2 4 8 180 323 71 | 66 -6.995 38.239 23 17 1 4 1 2 4 8 222 322 72 | 67 74.084 -34.216 20 5 1 4 1 2 4 8 109 253 73 | 68 14.301 -0.800 5 7 1 4 1 2 4 8 237 392 74 | 69 -94.141 -48.779 14 1 1 4 1 2 4 8 178 283 75 | 70 15.332 -42.169 1 3 1 4 1 2 4 8 285 405 76 | 71 -46.637 -16.461 10 10 1 4 1 2 4 8 457 606 77 | 72 -0.958 -81.000 3 5 1 4 1 2 4 8 300 416 78 | 73 -37.445 -42.505 4 1 1 4 1 2 4 8 129 299 79 | 74 -3.680 -69.073 23 17 1 4 1 2 4 8 372 511 80 | 75 34.894 -20.898 21 14 1 4 1 2 4 8 95 260 81 | 76 45.544 51.410 3 4 1 4 1 2 4 8 122 251 82 | 77 -31.927 88.239 21 10 1 4 1 2 4 8 233 387 83 | 78 -13.580 -26.959 25 25 1 4 1 2 4 8 306 411 84 | 79 12.653 -48.340 5 11 1 4 1 2 4 8 129 234 85 | 80 -46.179 -38.269 11 16 1 4 1 2 4 8 248 346 86 | 81 -29.620 35.083 10 25 1 4 1 2 4 8 106 251 87 | 82 13.696 43.988 5 20 1 4 1 2 4 8 410 570 88 | 83 38.342 -46.338 3 3 1 4 1 2 4 8 432 539 89 | 84 -26.227 24.506 13 7 1 4 1 2 4 8 350 455 90 | 85 90.009 -43.640 19 2 1 4 1 2 4 8 190 339 91 | 86 -6.995 22.522 13 6 1 4 1 2 4 8 110 257 92 | 87 -29.993 -28.851 8 14 1 4 1 2 4 8 383 531 93 | 88 -56.268 -64.740 13 14 1 4 1 2 4 8 324 442 94 | 89 -87.415 90.796 15 2 1 4 1 2 4 8 158 285 95 | 90 -5.762 54.034 10 15 1 4 1 2 4 8 272 413 96 | 91 -34.845 -27.185 4 22 1 4 1 2 4 8 404 554 97 | 92 -16.479 16.180 23 4 1 4 1 2 4 8 82 254 98 | 93 23.712 29.492 23 22 1 4 1 2 4 8 216 370 99 | 94 -40.991 46.594 14 19 1 4 1 2 4 8 442 553 100 | 95 -53.516 -21.619 20 5 1 4 1 2 4 8 85 192 101 | 96 -17.560 37.494 3 10 1 4 1 2 4 8 124 269 102 | 97 -37.311 56.836 5 4 1 4 1 2 4 8 242 411 103 | 98 71.515 22.510 16 7 1 4 1 2 4 8 238 394 104 | 99 -41.864 27.710 21 11 1 4 1 2 4 8 447 612 105 | 100 14.819 -82.117 4 14 1 4 1 2 4 8 387 483 106 | 101 30.316 -55.322 2 2 1 4 1 2 4 8 148 257 107 | 102 -39.172 47.998 11 12 1 4 1 2 4 8 267 419 108 | 103 -94.415 15.192 15 12 1 4 1 2 4 8 282 384 109 | 104 -7.806 11.273 16 24 1 4 1 2 4 8 288 397 110 | 105 28.571 -49.908 3 8 1 4 1 2 4 8 169 343 111 | 106 -20.654 24.554 21 4 1 4 1 2 4 8 466 604 112 | 107 -30.963 -18.903 21 25 1 4 1 2 4 8 418 521 113 | 108 1.721 -20.447 25 6 1 4 1 2 4 8 411 571 114 | 109 -31.012 -21.106 12 9 1 4 1 2 4 8 282 416 115 | 110 -85.718 -28.015 21 23 1 4 1 2 4 8 80 202 116 | 111 58.826 -63.043 6 15 1 4 1 2 4 8 367 494 117 | 112 -15.753 -52.686 2 7 1 4 1 2 4 8 345 462 118 | 113 -71.661 51.184 24 6 1 4 1 2 4 8 392 499 119 | 114 -92.633 -6.598 21 5 1 4 1 2 4 8 94 184 120 | 115 33.508 -49.255 24 12 1 4 1 2 4 8 386 506 121 | 116 59.314 54.095 11 3 1 4 1 2 4 8 263 386 122 | 117 30.737 -28.436 12 22 1 4 1 2 4 8 61 175 123 | 118 -55.896 18.457 22 18 1 4 1 2 4 8 170 312 124 | 119 -89.960 -39.532 19 16 1 4 1 2 4 8 124 296 125 | 120 36.438 20.819 8 5 1 4 1 2 4 8 340 466 126 | 121 -53.674 -56.427 9 1 1 4 1 2 4 8 273 391 127 | 122 -31.866 32.538 10 8 1 4 1 2 4 8 130 253 128 | 123 5.658 12.756 5 20 1 4 1 2 4 8 154 318 129 | 124 -52.905 42.804 14 17 1 4 1 2 4 8 124 274 130 | 125 47.131 -45.465 7 11 1 4 1 2 4 8 119 211 131 | 126 30.792 9.869 5 8 1 4 1 2 4 8 150 245 132 | 127 -45.209 -56.293 22 15 1 4 1 2 4 8 98 269 133 | 128 10.919 13.306 5 16 1 4 1 2 4 8 367 525 134 | 129 -32.990 -34.003 1 6 1 4 1 2 4 8 134 262 135 | 130 -40.869 30.579 9 24 1 4 1 2 4 8 459 586 136 | 131 -69.348 80.408 16 10 1 4 1 2 4 8 471 565 137 | 132 -58.966 -29.541 14 4 1 4 1 2 4 8 194 319 138 | 133 -36.847 -33.710 21 10 1 4 1 2 4 8 134 256 139 | 134 -21.820 65.369 8 17 1 4 1 2 4 8 164 276 140 | 135 22.980 -41.010 7 5 1 4 1 2 4 8 282 405 141 | 136 -77.557 59.814 24 14 1 4 1 2 4 8 89 238 142 | 137 -47.205 67.725 24 16 1 4 1 2 4 8 358 490 143 | 138 -21.606 35.168 16 11 1 4 1 2 4 8 211 376 144 | 139 -55.676 -31.384 4 25 1 4 1 2 4 8 456 624 145 | 140 -85.229 88.788 7 4 1 4 1 2 4 8 219 358 146 | 141 -3.918 -63.525 13 12 1 4 1 2 4 8 451 577 147 | 142 -58.557 59.375 25 19 1 4 1 2 4 8 415 568 148 | 143 -24.542 14.935 10 6 1 4 1 2 4 8 65 188 149 | 144 -48.779 16.968 24 23 1 4 1 2 4 8 372 543 150 | 145 -40.082 -24.780 0 0 0 0 0 1000 151 | 146 24.292 -27.704 0 0 0 0 0 1000 152 | 147 -45.877 41.092 0 0 0 0 0 1000 153 | 148 -11.896 15.875 0 0 0 0 0 1000 154 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr13.txt: -------------------------------------------------------------------------------- 1 | 6 3 144 4 2 | 460 190 3 | 460 190 4 | 460 190 5 | 460 190 6 | 1 -55.280 -24.371 25 9 1 4 1 2 4 8 204 515 7 | 2 -48.297 53.314 13 22 1 4 1 2 4 8 203 424 8 | 3 -49.072 -38.489 20 10 1 4 1 2 4 8 169 352 9 | 4 25.311 -18.561 16 24 1 4 1 2 4 8 108 433 10 | 5 -24.469 -3.815 4 25 1 4 1 2 4 8 235 447 11 | 6 24.591 -17.896 2 6 1 4 1 2 4 8 296 573 12 | 7 -10.419 60.364 7 22 1 4 1 2 4 8 268 566 13 | 8 38.177 -35.175 15 1 1 4 1 2 4 8 221 401 14 | 9 -68.280 93.073 10 19 1 4 1 2 4 8 190 374 15 | 10 -30.792 -57.336 4 19 1 4 1 2 4 8 290 490 16 | 11 -37.061 -12.122 19 5 1 4 1 2 4 8 225 502 17 | 12 -15.741 -47.638 10 9 1 4 1 2 4 8 196 434 18 | 13 -63.379 -22.919 15 8 1 4 1 2 4 8 101 319 19 | 14 -43.109 -43.439 4 14 1 4 1 2 4 8 139 386 20 | 15 -25.623 13.599 17 4 1 4 1 2 4 8 251 592 21 | 16 -2.625 16.632 4 7 1 4 1 2 4 8 256 572 22 | 17 -41.577 -28.497 3 10 1 4 1 2 4 8 277 488 23 | 18 2.081 12.885 9 22 1 4 1 2 4 8 169 436 24 | 19 3.925 -47.845 25 17 1 4 1 2 4 8 123 364 25 | 20 -83.295 26.324 14 10 1 4 1 2 4 8 208 419 26 | 21 -6.458 26.355 23 8 1 4 1 2 4 8 172 434 27 | 22 3.290 6.732 7 14 1 4 1 2 4 8 213 428 28 | 23 -34.869 30.426 7 12 1 4 1 2 4 8 200 502 29 | 24 15.546 -36.273 1 17 1 4 1 2 4 8 122 450 30 | 25 12.842 5.127 10 19 1 4 1 2 4 8 115 431 31 | 26 -48.450 -24.426 15 25 1 4 1 2 4 8 156 350 32 | 27 -88.538 -10.461 1 25 1 4 1 2 4 8 258 460 33 | 28 -29.773 0.995 2 6 1 4 1 2 4 8 263 498 34 | 29 9.827 38.416 9 21 1 4 1 2 4 8 120 349 35 | 30 1.410 92.938 10 3 1 4 1 2 4 8 215 418 36 | 31 -79.303 15.381 2 19 1 4 1 2 4 8 200 538 37 | 32 -30.652 40.063 23 16 1 4 1 2 4 8 274 602 38 | 33 18.927 21.637 15 21 1 4 1 2 4 8 109 377 39 | 34 45.087 -59.906 13 17 1 4 1 2 4 8 221 535 40 | 35 -34.949 -3.815 16 19 1 4 1 2 4 8 274 465 41 | 36 81.201 -74.744 21 6 1 4 1 2 4 8 246 583 42 | 37 15.594 -28.455 1 10 1 4 1 2 4 8 113 348 43 | 38 -72.192 29.547 18 22 1 4 1 2 4 8 80 276 44 | 39 -42.914 -22.675 17 17 1 4 1 2 4 8 285 472 45 | 40 -76.392 -57.489 13 16 1 4 1 2 4 8 81 309 46 | 41 -28.540 12.073 5 5 1 4 1 2 4 8 187 466 47 | 42 -61.389 26.526 23 1 1 4 1 2 4 8 196 521 48 | 43 -8.472 12.616 20 21 1 4 1 2 4 8 230 436 49 | 44 -61.475 33.392 1 20 1 4 1 2 4 8 261 546 50 | 45 -34.674 -27.588 2 6 1 4 1 2 4 8 70 408 51 | 46 10.315 -12.518 15 1 1 4 1 2 4 8 192 397 52 | 47 -83.014 77.002 20 11 1 4 1 2 4 8 170 479 53 | 48 -40.417 49.988 9 22 1 4 1 2 4 8 193 540 54 | 49 83.832 33.905 13 25 1 4 1 2 4 8 249 431 55 | 50 -20.563 -75.830 4 18 1 4 1 2 4 8 231 487 56 | 51 15.442 -18.719 15 9 1 4 1 2 4 8 89 374 57 | 52 -59.937 -65.802 5 23 1 4 1 2 4 8 117 316 58 | 53 5.151 47.815 8 17 1 4 1 2 4 8 108 356 59 | 54 46.008 14.990 19 4 1 4 1 2 4 8 157 499 60 | 55 82.977 -1.660 16 1 1 4 1 2 4 8 182 517 61 | 56 39.893 -38.916 11 14 1 4 1 2 4 8 111 325 62 | 57 90.839 -83.539 23 11 1 4 1 2 4 8 138 346 63 | 58 7.068 0.067 14 7 1 4 1 2 4 8 279 638 64 | 59 18.958 40.088 24 21 1 4 1 2 4 8 172 383 65 | 60 -80.487 58.209 5 3 1 4 1 2 4 8 261 515 66 | 61 64.459 -62.946 5 18 1 4 1 2 4 8 269 560 67 | 62 -43.616 -26.117 23 5 1 4 1 2 4 8 208 548 68 | 63 -18.408 10.303 3 6 1 4 1 2 4 8 266 482 69 | 64 -43.921 63.251 10 22 1 4 1 2 4 8 81 344 70 | 65 11.353 26.221 23 13 1 4 1 2 4 8 93 437 71 | 66 -6.995 38.239 23 17 1 4 1 2 4 8 66 338 72 | 67 74.084 -34.216 20 5 1 4 1 2 4 8 164 462 73 | 68 14.301 -0.800 5 7 1 4 1 2 4 8 68 278 74 | 69 -94.141 -48.779 14 1 1 4 1 2 4 8 258 474 75 | 70 15.332 -42.169 1 3 1 4 1 2 4 8 219 562 76 | 71 -46.637 -16.461 10 10 1 4 1 2 4 8 200 488 77 | 72 -0.958 -81.000 3 5 1 4 1 2 4 8 99 373 78 | 73 -37.445 -42.505 4 1 1 4 1 2 4 8 234 444 79 | 74 -3.680 -69.073 23 17 1 4 1 2 4 8 265 615 80 | 75 34.894 -20.898 21 14 1 4 1 2 4 8 288 491 81 | 76 45.544 51.410 3 4 1 4 1 2 4 8 207 407 82 | 77 -31.927 88.239 21 10 1 4 1 2 4 8 125 470 83 | 78 -13.580 -26.959 25 25 1 4 1 2 4 8 152 410 84 | 79 12.653 -48.340 5 11 1 4 1 2 4 8 284 546 85 | 80 -46.179 -38.269 11 16 1 4 1 2 4 8 121 465 86 | 81 -29.620 35.083 10 25 1 4 1 2 4 8 294 511 87 | 82 13.696 43.988 5 20 1 4 1 2 4 8 62 351 88 | 83 38.342 -46.338 3 3 1 4 1 2 4 8 72 319 89 | 84 -26.227 24.506 13 7 1 4 1 2 4 8 217 475 90 | 85 90.009 -43.640 19 2 1 4 1 2 4 8 239 439 91 | 86 -6.995 22.522 13 6 1 4 1 2 4 8 271 626 92 | 87 -29.993 -28.851 8 14 1 4 1 2 4 8 105 294 93 | 88 -56.268 -64.740 13 14 1 4 1 2 4 8 280 514 94 | 89 -87.415 90.796 15 2 1 4 1 2 4 8 72 256 95 | 90 -5.762 54.034 10 15 1 4 1 2 4 8 249 527 96 | 91 -34.845 -27.185 4 22 1 4 1 2 4 8 237 465 97 | 92 -16.479 16.180 23 4 1 4 1 2 4 8 153 357 98 | 93 23.712 29.492 23 22 1 4 1 2 4 8 298 487 99 | 94 -40.991 46.594 14 19 1 4 1 2 4 8 147 407 100 | 95 -53.516 -21.619 20 5 1 4 1 2 4 8 195 547 101 | 96 -17.560 37.494 3 10 1 4 1 2 4 8 126 461 102 | 97 -37.311 56.836 5 4 1 4 1 2 4 8 117 433 103 | 98 71.515 22.510 16 7 1 4 1 2 4 8 71 412 104 | 99 -41.864 27.710 21 11 1 4 1 2 4 8 274 482 105 | 100 14.819 -82.117 4 14 1 4 1 2 4 8 78 329 106 | 101 30.316 -55.322 2 2 1 4 1 2 4 8 257 493 107 | 102 -39.172 47.998 11 12 1 4 1 2 4 8 71 351 108 | 103 -94.415 15.192 15 12 1 4 1 2 4 8 73 319 109 | 104 -7.806 11.273 16 24 1 4 1 2 4 8 285 583 110 | 105 28.571 -49.908 3 8 1 4 1 2 4 8 262 443 111 | 106 -20.654 24.554 21 4 1 4 1 2 4 8 79 308 112 | 107 -30.963 -18.903 21 25 1 4 1 2 4 8 87 304 113 | 108 1.721 -20.447 25 6 1 4 1 2 4 8 269 627 114 | 109 -31.012 -21.106 12 9 1 4 1 2 4 8 62 267 115 | 110 -85.718 -28.015 21 23 1 4 1 2 4 8 66 278 116 | 111 58.826 -63.043 6 15 1 4 1 2 4 8 141 414 117 | 112 -15.753 -52.686 2 7 1 4 1 2 4 8 262 620 118 | 113 -71.661 51.184 24 6 1 4 1 2 4 8 76 303 119 | 114 -92.633 -6.598 21 5 1 4 1 2 4 8 176 383 120 | 115 33.508 -49.255 24 12 1 4 1 2 4 8 206 529 121 | 116 59.314 54.095 11 3 1 4 1 2 4 8 234 526 122 | 117 30.737 -28.436 12 22 1 4 1 2 4 8 93 424 123 | 118 -55.896 18.457 22 18 1 4 1 2 4 8 92 400 124 | 119 -89.960 -39.532 19 16 1 4 1 2 4 8 212 397 125 | 120 36.438 20.819 8 5 1 4 1 2 4 8 97 422 126 | 121 -53.674 -56.427 9 1 1 4 1 2 4 8 268 491 127 | 122 -31.866 32.538 10 8 1 4 1 2 4 8 72 291 128 | 123 5.658 12.756 5 20 1 4 1 2 4 8 79 419 129 | 124 -52.905 42.804 14 17 1 4 1 2 4 8 115 351 130 | 125 47.131 -45.465 7 11 1 4 1 2 4 8 182 387 131 | 126 30.792 9.869 5 8 1 4 1 2 4 8 259 525 132 | 127 -45.209 -56.293 22 15 1 4 1 2 4 8 198 432 133 | 128 10.919 13.306 5 16 1 4 1 2 4 8 88 278 134 | 129 -32.990 -34.003 1 6 1 4 1 2 4 8 262 492 135 | 130 -40.869 30.579 9 24 1 4 1 2 4 8 211 527 136 | 131 -69.348 80.408 16 10 1 4 1 2 4 8 197 450 137 | 132 -58.966 -29.541 14 4 1 4 1 2 4 8 182 501 138 | 133 -36.847 -33.710 21 10 1 4 1 2 4 8 298 582 139 | 134 -21.820 65.369 8 17 1 4 1 2 4 8 76 271 140 | 135 22.980 -41.010 7 5 1 4 1 2 4 8 83 421 141 | 136 -77.557 59.814 24 14 1 4 1 2 4 8 290 482 142 | 137 -47.205 67.725 24 16 1 4 1 2 4 8 85 390 143 | 138 -21.606 35.168 16 11 1 4 1 2 4 8 61 247 144 | 139 -55.676 -31.384 4 25 1 4 1 2 4 8 148 438 145 | 140 -85.229 88.788 7 4 1 4 1 2 4 8 84 408 146 | 141 -3.918 -63.525 13 12 1 4 1 2 4 8 278 614 147 | 142 -58.557 59.375 25 19 1 4 1 2 4 8 104 427 148 | 143 -24.542 14.935 10 6 1 4 1 2 4 8 132 459 149 | 144 -48.779 16.968 24 23 1 4 1 2 4 8 236 582 150 | 145 -40.082 -24.780 0 0 0 0 0 1000 151 | 146 24.292 -27.704 0 0 0 0 0 1000 152 | 147 -45.877 41.092 0 0 0 0 0 1000 153 | 148 -11.896 15.875 0 0 0 0 0 1000 154 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr08.txt: -------------------------------------------------------------------------------- 1 | 6 3 144 6 2 | 475 190 3 | 475 190 4 | 475 190 5 | 475 190 6 | 475 190 7 | 475 190 8 | 1 -40.289 -42.303 6 20 1 6 1 2 4 8 16 32 434 571 9 | 2 -64.709 -17.389 3 10 1 6 1 2 4 8 16 32 131 233 10 | 3 5.060 -14.349 23 8 1 6 1 2 4 8 16 32 408 585 11 | 4 72.095 20.233 1 6 1 6 1 2 4 8 16 32 319 486 12 | 5 2.594 -15.002 18 1 1 6 1 2 4 8 16 32 211 371 13 | 6 -24.176 -72.894 8 19 1 6 1 2 4 8 16 32 268 399 14 | 7 -13.190 66.498 2 13 1 6 1 2 4 8 16 32 433 588 15 | 8 33.191 13.690 9 24 1 6 1 2 4 8 16 32 232 380 16 | 9 66.827 -30.554 1 15 1 6 1 2 4 8 16 32 361 521 17 | 10 19.934 35.883 20 13 1 6 1 2 4 8 16 32 433 590 18 | 11 -27.771 32.269 23 10 1 6 1 2 4 8 16 32 474 639 19 | 12 51.154 -16.827 22 4 1 6 1 2 4 8 16 32 73 185 20 | 13 -19.214 23.749 24 12 1 6 1 2 4 8 16 32 194 310 21 | 14 -46.643 -52.954 21 21 1 6 1 2 4 8 16 32 107 243 22 | 15 -10.590 -16.626 18 2 1 6 1 2 4 8 16 32 310 432 23 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6 1 2 4 8 16 32 148 448 144 | 137 -86.975 10.345 16 8 1 6 1 2 4 8 16 32 268 597 145 | 138 -47.272 -46.326 23 22 1 6 1 2 4 8 16 32 205 527 146 | 139 4.095 -19.354 13 7 1 6 1 2 4 8 16 32 163 356 147 | 140 4.205 30.292 7 15 1 6 1 2 4 8 16 32 180 376 148 | 141 -42.017 53.986 20 22 1 6 1 2 4 8 16 32 88 360 149 | 142 35.095 -10.895 25 7 1 6 1 2 4 8 16 32 238 427 150 | 143 61.188 -59.875 12 14 1 6 1 2 4 8 16 32 229 520 151 | 144 -34.674 -1.337 9 21 1 6 1 2 4 8 16 32 289 555 152 | 145 -3.799 44.290 0 0 0 0 0 1000 153 | 146 14.233 21.173 0 0 0 0 0 1000 154 | 147 -23.334 -28.397 0 0 0 0 0 1000 155 | 148 10.065 1.822 0 0 0 0 0 1000 156 | 149 -49.115 -43.549 0 0 0 0 0 1000 157 | 150 -21.054 4.144 0 0 0 0 0 1000 158 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr04.txt: -------------------------------------------------------------------------------- 1 | 6 5 192 4 2 | 440 185 3 | 440 185 4 | 440 185 5 | 440 185 6 | 1 -44.629 -55.640 6 11 1 4 1 2 4 8 206 382 7 | 2 36.096 64.935 16 11 1 4 1 2 4 8 126 266 8 | 3 -25.470 44.580 7 23 1 4 1 2 4 8 189 280 9 | 4 -33.954 -73.059 20 16 1 4 1 2 4 8 472 650 10 | 5 45.654 35.730 7 16 1 4 1 2 4 8 474 579 11 | 6 14.954 -36.719 8 2 1 4 1 2 4 8 214 373 12 | 7 -1.477 47.205 4 1 1 4 1 2 4 8 301 437 13 | 8 2.155 -40.912 23 25 1 4 1 2 4 8 223 350 14 | 9 22.992 1.398 4 24 1 4 1 2 4 8 220 331 15 | 10 -28.857 5.957 5 14 1 4 1 2 4 8 472 563 16 | 11 -48.120 94.116 15 6 1 4 1 2 4 8 225 327 17 | 12 7.239 17.322 12 20 1 4 1 2 4 8 172 303 18 | 13 4.614 27.051 12 10 1 4 1 2 4 8 151 288 19 | 14 -13.971 28.198 18 11 1 4 1 2 4 8 433 562 20 | 15 12.946 -11.688 19 2 1 4 1 2 4 8 478 614 21 | 16 -36.670 23.102 17 22 1 4 1 2 4 8 340 431 22 | 17 -4.224 -49.310 2 2 1 4 1 2 4 8 224 367 23 | 18 1.093 -7.135 15 12 1 4 1 2 4 8 240 370 24 | 19 -56.958 27.905 10 9 1 4 1 2 4 8 160 290 25 | 20 -80.994 -77.686 3 21 1 4 1 2 4 8 214 383 26 | 21 72.260 59.027 24 10 1 4 1 2 4 8 455 565 27 | 22 -21.655 1.636 1 13 1 4 1 2 4 8 381 551 28 | 23 -20.154 -62.415 22 25 1 4 1 2 4 8 154 317 29 | 24 -48.157 6.287 22 9 1 4 1 2 4 8 239 354 30 | 25 -5.078 61.597 17 19 1 4 1 2 4 8 200 331 31 | 26 17.145 -47.046 7 22 1 4 1 2 4 8 336 435 32 | 27 -16.064 8.557 11 4 1 4 1 2 4 8 171 304 33 | 28 6.683 -27.069 4 4 1 4 1 2 4 8 122 249 34 | 29 15.027 -34.979 15 7 1 4 1 2 4 8 115 227 35 | 30 37.946 73.914 15 20 1 4 1 2 4 8 310 468 36 | 31 -41.199 -43.018 9 17 1 4 1 2 4 8 164 259 37 | 32 32.538 11.786 15 19 1 4 1 2 4 8 229 384 38 | 33 -20.306 -20.239 1 12 1 4 1 2 4 8 78 195 39 | 34 -35.480 19.135 13 2 1 4 1 2 4 8 191 343 40 | 35 58.038 56.659 17 13 1 4 1 2 4 8 214 349 41 | 36 -36.694 -6.256 17 24 1 4 1 2 4 8 192 294 42 | 37 0.446 -46.735 25 22 1 4 1 2 4 8 199 335 43 | 38 -19.690 38.977 5 17 1 4 1 2 4 8 339 476 44 | 39 -47.339 20.911 25 19 1 4 1 2 4 8 153 296 45 | 40 -79.236 -57.678 2 4 1 4 1 2 4 8 209 315 46 | 41 5.597 -34.076 9 12 1 4 1 2 4 8 318 451 47 | 42 34.454 -8.289 7 22 1 4 1 2 4 8 440 549 48 | 43 18.500 -51.154 5 2 1 4 1 2 4 8 184 306 49 | 44 -4.346 -44.525 3 19 1 4 1 2 4 8 386 557 50 | 45 -14.148 -74.164 17 3 1 4 1 2 4 8 72 231 51 | 46 19.867 77.515 12 5 1 4 1 2 4 8 188 317 52 | 47 -50.079 17.871 21 22 1 4 1 2 4 8 407 522 53 | 48 42.786 -79.883 18 6 1 4 1 2 4 8 245 400 54 | 49 1.520 -29.034 8 9 1 4 1 2 4 8 385 485 55 | 50 14.728 -3.436 14 8 1 4 1 2 4 8 137 306 56 | 51 -26.166 -48.999 22 24 1 4 1 2 4 8 68 196 57 | 52 -63.873 -5.273 16 15 1 4 1 2 4 8 71 178 58 | 53 -54.138 7.086 21 16 1 4 1 2 4 8 72 204 59 | 54 17.932 -34.094 7 9 1 4 1 2 4 8 177 300 60 | 55 -98.706 34.552 12 1 1 4 1 2 4 8 339 467 61 | 56 28.802 -8.582 2 18 1 4 1 2 4 8 307 443 62 | 57 2.716 90.594 5 17 1 4 1 2 4 8 164 339 63 | 58 -31.024 48.938 3 18 1 4 1 2 4 8 277 450 64 | 59 -46.545 42.316 11 2 1 4 1 2 4 8 107 231 65 | 60 27.289 58.179 2 25 1 4 1 2 4 8 278 373 66 | 61 28.815 -28.027 7 21 1 4 1 2 4 8 407 554 67 | 62 -26.099 11.328 20 19 1 4 1 2 4 8 123 273 68 | 63 0.537 -26.221 7 10 1 4 1 2 4 8 322 466 69 | 64 -50.458 7.812 11 2 1 4 1 2 4 8 425 575 70 | 65 30.005 19.092 24 21 1 4 1 2 4 8 122 235 71 | 66 -12.604 22.058 15 20 1 4 1 2 4 8 170 310 72 | 67 22.009 -8.636 5 21 1 4 1 2 4 8 109 216 73 | 68 8.942 2.875 25 2 1 4 1 2 4 8 78 251 74 | 69 -19.812 0.922 12 6 1 4 1 2 4 8 477 634 75 | 70 -4.230 31.250 22 2 1 4 1 2 4 8 338 435 76 | 71 17.975 -0.409 11 12 1 4 1 2 4 8 166 308 77 | 72 -2.350 42.938 16 12 1 4 1 2 4 8 429 534 78 | 73 -16.516 68.085 10 5 1 4 1 2 4 8 203 355 79 | 74 -1.190 42.023 3 5 1 4 1 2 4 8 119 278 80 | 75 56.635 -44.019 10 8 1 4 1 2 4 8 450 620 81 | 76 57.483 35.504 17 15 1 4 1 2 4 8 100 236 82 | 77 -26.099 7.477 2 8 1 4 1 2 4 8 285 430 83 | 78 7.489 -57.239 21 7 1 4 1 2 4 8 106 249 84 | 79 -22.095 55.780 16 14 1 4 1 2 4 8 116 220 85 | 80 19.867 38.824 6 18 1 4 1 2 4 8 107 258 86 | 81 5.310 49.219 12 17 1 4 1 2 4 8 407 513 87 | 82 15.155 6.299 23 5 1 4 1 2 4 8 317 429 88 | 83 5.481 -31.500 23 6 1 4 1 2 4 8 132 267 89 | 84 7.959 -28.662 18 9 1 4 1 2 4 8 167 319 90 | 85 -75.812 4.010 18 24 1 4 1 2 4 8 179 290 91 | 86 -43.671 -61.646 14 13 1 4 1 2 4 8 360 477 92 | 87 -2.576 -62.140 2 5 1 4 1 2 4 8 173 269 93 | 88 -18.842 -36.884 11 4 1 4 1 2 4 8 198 332 94 | 89 20.844 21.167 24 22 1 4 1 2 4 8 103 262 95 | 90 3.137 -19.220 14 24 1 4 1 2 4 8 168 280 96 | 91 -20.361 18.878 3 25 1 4 1 2 4 8 296 469 97 | 92 -0.897 -54.938 21 9 1 4 1 2 4 8 296 419 98 | 93 -29.553 9.277 8 8 1 4 1 2 4 8 169 322 99 | 94 5.048 -35.046 19 8 1 4 1 2 4 8 61 181 100 | 95 27.759 48.712 7 25 1 4 1 2 4 8 245 408 101 | 96 12.079 -42.346 19 7 1 4 1 2 4 8 454 574 102 | 97 -51.270 41.144 23 24 1 4 1 2 4 8 295 434 103 | 98 -29.077 4.291 8 16 1 4 1 2 4 8 119 248 104 | 99 -18.555 -14.819 18 15 1 4 1 2 4 8 183 342 105 | 100 7.855 -49.451 7 7 1 4 1 2 4 8 434 553 106 | 101 -19.116 -32.086 1 17 1 4 1 2 4 8 82 175 107 | 102 17.651 -52.271 9 9 1 4 1 2 4 8 135 283 108 | 103 16.907 -62.964 5 14 1 4 1 2 4 8 311 454 109 | 104 18.988 6.219 23 20 1 4 1 2 4 8 217 333 110 | 105 -68.878 -64.539 18 6 1 4 1 2 4 8 410 542 111 | 106 -13.361 20.264 5 17 1 4 1 2 4 8 143 306 112 | 107 -44.440 63.721 17 23 1 4 1 2 4 8 233 347 113 | 108 -6.500 71.436 9 14 1 4 1 2 4 8 195 300 114 | 109 -51.978 25.409 19 19 1 4 1 2 4 8 143 241 115 | 110 -0.739 59.491 20 21 1 4 1 2 4 8 268 445 116 | 111 -64.679 83.038 1 6 1 4 1 2 4 8 154 257 117 | 112 -11.273 -35.944 18 1 1 4 1 2 4 8 255 406 118 | 113 -16.864 7.776 15 22 1 4 1 2 4 8 142 254 119 | 114 19.843 -49.805 10 7 1 4 1 2 4 8 311 452 120 | 115 -16.284 -2.936 8 14 1 4 1 2 4 8 137 238 121 | 116 8.356 67.041 14 7 1 4 1 2 4 8 281 381 122 | 117 30.878 24.316 15 25 1 4 1 2 4 8 289 419 123 | 118 93.524 -20.233 17 17 1 4 1 2 4 8 329 478 124 | 119 -9.332 -56.177 9 10 1 4 1 2 4 8 359 504 125 | 120 -34.442 -63.312 13 23 1 4 1 2 4 8 398 566 126 | 121 20.941 -5.646 18 22 1 4 1 2 4 8 448 562 127 | 122 -3.418 89.594 20 3 1 4 1 2 4 8 115 288 128 | 123 -18.738 45.227 8 14 1 4 1 2 4 8 261 387 129 | 124 2.435 -30.524 2 14 1 4 1 2 4 8 112 287 130 | 125 -28.864 56.616 7 2 1 4 1 2 4 8 329 439 131 | 126 76.880 -29.901 6 2 1 4 1 2 4 8 100 241 132 | 127 -90.466 10.278 12 21 1 4 1 2 4 8 376 506 133 | 128 -35.101 46.545 12 10 1 4 1 2 4 8 121 232 134 | 129 -48.395 43.854 25 10 1 4 1 2 4 8 343 468 135 | 130 -40.997 26.703 6 5 1 4 1 2 4 8 302 399 136 | 131 13.696 34.607 5 9 1 4 1 2 4 8 118 245 137 | 132 -71.472 11.450 20 15 1 4 1 2 4 8 93 257 138 | 133 -36.230 51.904 2 19 1 4 1 2 4 8 101 192 139 | 134 -39.465 4.907 25 7 1 4 1 2 4 8 282 453 140 | 135 -16.565 23.773 7 11 1 4 1 2 4 8 347 461 141 | 136 10.754 -28.552 6 2 1 4 1 2 4 8 376 554 142 | 137 -23.029 47.015 21 25 1 4 1 2 4 8 422 592 143 | 138 -8.807 21.216 19 14 1 4 1 2 4 8 93 272 144 | 139 -40.039 27.435 22 15 1 4 1 2 4 8 209 349 145 | 140 -37.445 -41.040 8 12 1 4 1 2 4 8 85 193 146 | 141 48.224 0.085 19 11 1 4 1 2 4 8 469 624 147 | 142 -0.323 68.237 6 17 1 4 1 2 4 8 126 229 148 | 143 -14.294 -33.423 24 4 1 4 1 2 4 8 435 591 149 | 144 -1.440 -34.814 15 24 1 4 1 2 4 8 390 481 150 | 145 -34.454 42.346 25 8 1 4 1 2 4 8 72 188 151 | 146 8.679 -66.180 6 20 1 4 1 2 4 8 278 399 152 | 147 -61.249 -51.367 21 12 1 4 1 2 4 8 80 189 153 | 148 -1.093 9.912 4 10 1 4 1 2 4 8 402 553 154 | 149 -16.565 66.547 10 12 1 4 1 2 4 8 120 254 155 | 150 8.435 52.838 16 9 1 4 1 2 4 8 111 280 156 | 151 -22.034 -36.896 12 16 1 4 1 2 4 8 133 262 157 | 152 15.765 17.322 13 23 1 4 1 2 4 8 348 513 158 | 153 9.003 -18.695 2 15 1 4 1 2 4 8 156 270 159 | 154 -4.584 3.491 3 10 1 4 1 2 4 8 437 561 160 | 155 8.875 40.662 10 21 1 4 1 2 4 8 156 304 161 | 156 26.093 -33.966 18 2 1 4 1 2 4 8 294 465 162 | 157 -32.501 39.526 18 16 1 4 1 2 4 8 344 477 163 | 158 -32.629 -82.581 11 15 1 4 1 2 4 8 427 580 164 | 159 -98.486 22.021 7 11 1 4 1 2 4 8 106 263 165 | 160 -59.052 4.163 18 11 1 4 1 2 4 8 283 413 166 | 161 7.428 -2.570 15 17 1 4 1 2 4 8 223 358 167 | 162 -59.601 12.469 5 3 1 4 1 2 4 8 355 512 168 | 163 -11.224 -31.830 20 9 1 4 1 2 4 8 225 389 169 | 164 -16.809 -35.040 9 1 1 4 1 2 4 8 201 327 170 | 165 21.863 18.140 22 13 1 4 1 2 4 8 147 313 171 | 166 45.038 -61.316 13 1 1 4 1 2 4 8 285 381 172 | 167 -48.816 25.568 24 2 1 4 1 2 4 8 245 399 173 | 168 -32.184 -16.736 4 22 1 4 1 2 4 8 238 341 174 | 169 12.439 -41.492 16 10 1 4 1 2 4 8 353 493 175 | 170 -21.082 -77.075 16 4 1 4 1 2 4 8 182 311 176 | 171 -17.822 -18.518 14 9 1 4 1 2 4 8 429 548 177 | 172 48.334 93.518 14 11 1 4 1 2 4 8 229 362 178 | 173 -18.677 49.005 25 3 1 4 1 2 4 8 195 334 179 | 174 12.140 30.042 23 19 1 4 1 2 4 8 180 288 180 | 175 3.113 -40.302 23 19 1 4 1 2 4 8 249 422 181 | 176 17.133 37.079 4 4 1 4 1 2 4 8 424 556 182 | 177 22.577 0.836 17 19 1 4 1 2 4 8 469 628 183 | 178 -30.426 52.942 12 15 1 4 1 2 4 8 168 312 184 | 179 33.765 -67.108 11 21 1 4 1 2 4 8 166 276 185 | 180 5.414 1.532 24 25 1 4 1 2 4 8 310 412 186 | 181 72.156 30.103 22 4 1 4 1 2 4 8 443 557 187 | 182 -24.109 -34.625 10 3 1 4 1 2 4 8 367 540 188 | 183 17.413 75.146 10 6 1 4 1 2 4 8 199 325 189 | 184 10.638 -60.052 10 6 1 4 1 2 4 8 114 209 190 | 185 -5.176 -48.999 5 21 1 4 1 2 4 8 77 178 191 | 186 -19.342 21.118 13 25 1 4 1 2 4 8 400 533 192 | 187 -46.802 -14.661 18 10 1 4 1 2 4 8 345 521 193 | 188 -16.779 43.439 25 25 1 4 1 2 4 8 359 525 194 | 189 14.093 -13.605 12 19 1 4 1 2 4 8 302 414 195 | 190 -21.063 26.019 18 19 1 4 1 2 4 8 177 318 196 | 191 39.813 -40.668 9 22 1 4 1 2 4 8 298 429 197 | 192 52.057 30.334 23 5 1 4 1 2 4 8 323 457 198 | 193 -0.140 7.266 0 0 0 0 0 1000 199 | 194 -23.138 48.450 0 0 0 0 0 1000 200 | 195 -26.102 16.809 0 0 0 0 0 1000 201 | 196 0.244 -35.892 0 0 0 0 0 1000 202 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr14.txt: -------------------------------------------------------------------------------- 1 | 6 4 192 4 2 | 440 185 3 | 440 185 4 | 440 185 5 | 440 185 6 | 1 -44.629 -55.640 6 11 1 4 1 2 4 8 87 312 7 | 2 36.096 64.935 16 11 1 4 1 2 4 8 204 399 8 | 3 -25.470 44.580 7 23 1 4 1 2 4 8 130 431 9 | 4 -33.954 -73.059 20 16 1 4 1 2 4 8 159 416 10 | 5 45.654 35.730 7 16 1 4 1 2 4 8 265 454 11 | 6 14.954 -36.719 8 2 1 4 1 2 4 8 227 426 12 | 7 -1.477 47.205 4 1 1 4 1 2 4 8 293 603 13 | 8 2.155 -40.912 23 25 1 4 1 2 4 8 63 378 14 | 9 22.992 1.398 4 24 1 4 1 2 4 8 137 473 15 | 10 -28.857 5.957 5 14 1 4 1 2 4 8 122 366 16 | 11 -48.120 94.116 15 6 1 4 1 2 4 8 273 532 17 | 12 7.239 17.322 12 20 1 4 1 2 4 8 177 523 18 | 13 4.614 27.051 12 10 1 4 1 2 4 8 189 513 19 | 14 -13.971 28.198 18 11 1 4 1 2 4 8 157 349 20 | 15 12.946 -11.688 19 2 1 4 1 2 4 8 219 491 21 | 16 -36.670 23.102 17 22 1 4 1 2 4 8 236 568 22 | 17 -4.224 -49.310 2 2 1 4 1 2 4 8 235 459 23 | 18 1.093 -7.135 15 12 1 4 1 2 4 8 292 563 24 | 19 -56.958 27.905 10 9 1 4 1 2 4 8 92 325 25 | 20 -80.994 -77.686 3 21 1 4 1 2 4 8 212 455 26 | 21 72.260 59.027 24 10 1 4 1 2 4 8 100 391 27 | 22 -21.655 1.636 1 13 1 4 1 2 4 8 215 482 28 | 23 -20.154 -62.415 22 25 1 4 1 2 4 8 158 505 29 | 24 -48.157 6.287 22 9 1 4 1 2 4 8 268 560 30 | 25 -5.078 61.597 17 19 1 4 1 2 4 8 171 498 31 | 26 17.145 -47.046 7 22 1 4 1 2 4 8 169 476 32 | 27 -16.064 8.557 11 4 1 4 1 2 4 8 135 362 33 | 28 6.683 -27.069 4 4 1 4 1 2 4 8 77 350 34 | 29 15.027 -34.979 15 7 1 4 1 2 4 8 104 404 35 | 30 37.946 73.914 15 20 1 4 1 2 4 8 203 390 36 | 31 -41.199 -43.018 9 17 1 4 1 2 4 8 114 424 37 | 32 32.538 11.786 15 19 1 4 1 2 4 8 151 465 38 | 33 -20.306 -20.239 1 12 1 4 1 2 4 8 271 485 39 | 34 -35.480 19.135 13 2 1 4 1 2 4 8 223 512 40 | 35 58.038 56.659 17 13 1 4 1 2 4 8 125 307 41 | 36 -36.694 -6.256 17 24 1 4 1 2 4 8 142 488 42 | 37 0.446 -46.735 25 22 1 4 1 2 4 8 170 483 43 | 38 -19.690 38.977 5 17 1 4 1 2 4 8 140 466 44 | 39 -47.339 20.911 25 19 1 4 1 2 4 8 275 584 45 | 40 -79.236 -57.678 2 4 1 4 1 2 4 8 114 386 46 | 41 5.597 -34.076 9 12 1 4 1 2 4 8 289 478 47 | 42 34.454 -8.289 7 22 1 4 1 2 4 8 106 322 48 | 43 18.500 -51.154 5 2 1 4 1 2 4 8 257 572 49 | 44 -4.346 -44.525 3 19 1 4 1 2 4 8 221 515 50 | 45 -14.148 -74.164 17 3 1 4 1 2 4 8 219 420 51 | 46 19.867 77.515 12 5 1 4 1 2 4 8 116 331 52 | 47 -50.079 17.871 21 22 1 4 1 2 4 8 146 454 53 | 48 42.786 -79.883 18 6 1 4 1 2 4 8 241 553 54 | 49 1.520 -29.034 8 9 1 4 1 2 4 8 217 397 55 | 50 14.728 -3.436 14 8 1 4 1 2 4 8 186 498 56 | 51 -26.166 -48.999 22 24 1 4 1 2 4 8 88 283 57 | 52 -63.873 -5.273 16 15 1 4 1 2 4 8 149 360 58 | 53 -54.138 7.086 21 16 1 4 1 2 4 8 217 479 59 | 54 17.932 -34.094 7 9 1 4 1 2 4 8 138 452 60 | 55 -98.706 34.552 12 1 1 4 1 2 4 8 76 317 61 | 56 28.802 -8.582 2 18 1 4 1 2 4 8 75 367 62 | 57 2.716 90.594 5 17 1 4 1 2 4 8 135 400 63 | 58 -31.024 48.938 3 18 1 4 1 2 4 8 195 473 64 | 59 -46.545 42.316 11 2 1 4 1 2 4 8 61 403 65 | 60 27.289 58.179 2 25 1 4 1 2 4 8 102 354 66 | 61 28.815 -28.027 7 21 1 4 1 2 4 8 261 562 67 | 62 -26.099 11.328 20 19 1 4 1 2 4 8 224 414 68 | 63 0.537 -26.221 7 10 1 4 1 2 4 8 149 503 69 | 64 -50.458 7.812 11 2 1 4 1 2 4 8 154 449 70 | 65 30.005 19.092 24 21 1 4 1 2 4 8 60 344 71 | 66 -12.604 22.058 15 20 1 4 1 2 4 8 276 629 72 | 67 22.009 -8.636 5 21 1 4 1 2 4 8 164 387 73 | 68 8.942 2.875 25 2 1 4 1 2 4 8 217 462 74 | 69 -19.812 0.922 12 6 1 4 1 2 4 8 189 510 75 | 70 -4.230 31.250 22 2 1 4 1 2 4 8 249 547 76 | 71 17.975 -0.409 11 12 1 4 1 2 4 8 274 549 77 | 72 -2.350 42.938 16 12 1 4 1 2 4 8 173 424 78 | 73 -16.516 68.085 10 5 1 4 1 2 4 8 276 631 79 | 74 -1.190 42.023 3 5 1 4 1 2 4 8 120 330 80 | 75 56.635 -44.019 10 8 1 4 1 2 4 8 252 464 81 | 76 57.483 35.504 17 15 1 4 1 2 4 8 194 408 82 | 77 -26.099 7.477 2 8 1 4 1 2 4 8 155 483 83 | 78 7.489 -57.239 21 7 1 4 1 2 4 8 277 611 84 | 79 -22.095 55.780 16 14 1 4 1 2 4 8 276 536 85 | 80 19.867 38.824 6 18 1 4 1 2 4 8 281 551 86 | 81 5.310 49.219 12 17 1 4 1 2 4 8 226 545 87 | 82 15.155 6.299 23 5 1 4 1 2 4 8 249 443 88 | 83 5.481 -31.500 23 6 1 4 1 2 4 8 140 443 89 | 84 7.959 -28.662 18 9 1 4 1 2 4 8 113 465 90 | 85 -75.812 4.010 18 24 1 4 1 2 4 8 233 537 91 | 86 -43.671 -61.646 14 13 1 4 1 2 4 8 173 468 92 | 87 -2.576 -62.140 2 5 1 4 1 2 4 8 226 490 93 | 88 -18.842 -36.884 11 4 1 4 1 2 4 8 207 472 94 | 89 20.844 21.167 24 22 1 4 1 2 4 8 281 531 95 | 90 3.137 -19.220 14 24 1 4 1 2 4 8 190 408 96 | 91 -20.361 18.878 3 25 1 4 1 2 4 8 175 448 97 | 92 -0.897 -54.938 21 9 1 4 1 2 4 8 276 560 98 | 93 -29.553 9.277 8 8 1 4 1 2 4 8 271 457 99 | 94 5.048 -35.046 19 8 1 4 1 2 4 8 169 447 100 | 95 27.759 48.712 7 25 1 4 1 2 4 8 287 570 101 | 96 12.079 -42.346 19 7 1 4 1 2 4 8 74 359 102 | 97 -51.270 41.144 23 24 1 4 1 2 4 8 147 403 103 | 98 -29.077 4.291 8 16 1 4 1 2 4 8 243 529 104 | 99 -18.555 -14.819 18 15 1 4 1 2 4 8 123 318 105 | 100 7.855 -49.451 7 7 1 4 1 2 4 8 105 403 106 | 101 -19.116 -32.086 1 17 1 4 1 2 4 8 220 423 107 | 102 17.651 -52.271 9 9 1 4 1 2 4 8 236 508 108 | 103 16.907 -62.964 5 14 1 4 1 2 4 8 89 417 109 | 104 18.988 6.219 23 20 1 4 1 2 4 8 244 557 110 | 105 -68.878 -64.539 18 6 1 4 1 2 4 8 160 436 111 | 106 -13.361 20.264 5 17 1 4 1 2 4 8 134 414 112 | 107 -44.440 63.721 17 23 1 4 1 2 4 8 220 458 113 | 108 -6.500 71.436 9 14 1 4 1 2 4 8 146 393 114 | 109 -51.978 25.409 19 19 1 4 1 2 4 8 222 535 115 | 110 -0.739 59.491 20 21 1 4 1 2 4 8 174 496 116 | 111 -64.679 83.038 1 6 1 4 1 2 4 8 259 484 117 | 112 -11.273 -35.944 18 1 1 4 1 2 4 8 211 456 118 | 113 -16.864 7.776 15 22 1 4 1 2 4 8 173 411 119 | 114 19.843 -49.805 10 7 1 4 1 2 4 8 60 370 120 | 115 -16.284 -2.936 8 14 1 4 1 2 4 8 64 348 121 | 116 8.356 67.041 14 7 1 4 1 2 4 8 244 489 122 | 117 30.878 24.316 15 25 1 4 1 2 4 8 223 405 123 | 118 93.524 -20.233 17 17 1 4 1 2 4 8 183 506 124 | 119 -9.332 -56.177 9 10 1 4 1 2 4 8 106 416 125 | 120 -34.442 -63.312 13 23 1 4 1 2 4 8 130 359 126 | 121 20.941 -5.646 18 22 1 4 1 2 4 8 128 343 127 | 122 -3.418 89.594 20 3 1 4 1 2 4 8 63 383 128 | 123 -18.738 45.227 8 14 1 4 1 2 4 8 140 405 129 | 124 2.435 -30.524 2 14 1 4 1 2 4 8 108 393 130 | 125 -28.864 56.616 7 2 1 4 1 2 4 8 62 302 131 | 126 76.880 -29.901 6 2 1 4 1 2 4 8 231 589 132 | 127 -90.466 10.278 12 21 1 4 1 2 4 8 116 447 133 | 128 -35.101 46.545 12 10 1 4 1 2 4 8 188 465 134 | 129 -48.395 43.854 25 10 1 4 1 2 4 8 142 366 135 | 130 -40.997 26.703 6 5 1 4 1 2 4 8 121 377 136 | 131 13.696 34.607 5 9 1 4 1 2 4 8 235 555 137 | 132 -71.472 11.450 20 15 1 4 1 2 4 8 125 342 138 | 133 -36.230 51.904 2 19 1 4 1 2 4 8 279 500 139 | 134 -39.465 4.907 25 7 1 4 1 2 4 8 144 458 140 | 135 -16.565 23.773 7 11 1 4 1 2 4 8 120 439 141 | 136 10.754 -28.552 6 2 1 4 1 2 4 8 214 463 142 | 137 -23.029 47.015 21 25 1 4 1 2 4 8 121 323 143 | 138 -8.807 21.216 19 14 1 4 1 2 4 8 234 447 144 | 139 -40.039 27.435 22 15 1 4 1 2 4 8 247 481 145 | 140 -37.445 -41.040 8 12 1 4 1 2 4 8 247 509 146 | 141 48.224 0.085 19 11 1 4 1 2 4 8 288 503 147 | 142 -0.323 68.237 6 17 1 4 1 2 4 8 179 403 148 | 143 -14.294 -33.423 24 4 1 4 1 2 4 8 103 315 149 | 144 -1.440 -34.814 15 24 1 4 1 2 4 8 247 433 150 | 145 -34.454 42.346 25 8 1 4 1 2 4 8 90 435 151 | 146 8.679 -66.180 6 20 1 4 1 2 4 8 275 518 152 | 147 -61.249 -51.367 21 12 1 4 1 2 4 8 273 589 153 | 148 -1.093 9.912 4 10 1 4 1 2 4 8 84 360 154 | 149 -16.565 66.547 10 12 1 4 1 2 4 8 145 437 155 | 150 8.435 52.838 16 9 1 4 1 2 4 8 150 390 156 | 151 -22.034 -36.896 12 16 1 4 1 2 4 8 297 484 157 | 152 15.765 17.322 13 23 1 4 1 2 4 8 148 437 158 | 153 9.003 -18.695 2 15 1 4 1 2 4 8 279 465 159 | 154 -4.584 3.491 3 10 1 4 1 2 4 8 82 406 160 | 155 8.875 40.662 10 21 1 4 1 2 4 8 220 512 161 | 156 26.093 -33.966 18 2 1 4 1 2 4 8 190 450 162 | 157 -32.501 39.526 18 16 1 4 1 2 4 8 159 456 163 | 158 -32.629 -82.581 11 15 1 4 1 2 4 8 241 492 164 | 159 -98.486 22.021 7 11 1 4 1 2 4 8 67 348 165 | 160 -59.052 4.163 18 11 1 4 1 2 4 8 170 526 166 | 161 7.428 -2.570 15 17 1 4 1 2 4 8 257 446 167 | 162 -59.601 12.469 5 3 1 4 1 2 4 8 268 609 168 | 163 -11.224 -31.830 20 9 1 4 1 2 4 8 135 325 169 | 164 -16.809 -35.040 9 1 1 4 1 2 4 8 238 583 170 | 165 21.863 18.140 22 13 1 4 1 2 4 8 262 447 171 | 166 45.038 -61.316 13 1 1 4 1 2 4 8 135 358 172 | 167 -48.816 25.568 24 2 1 4 1 2 4 8 165 354 173 | 168 -32.184 -16.736 4 22 1 4 1 2 4 8 87 389 174 | 169 12.439 -41.492 16 10 1 4 1 2 4 8 222 514 175 | 170 -21.082 -77.075 16 4 1 4 1 2 4 8 148 393 176 | 171 -17.822 -18.518 14 9 1 4 1 2 4 8 296 605 177 | 172 48.334 93.518 14 11 1 4 1 2 4 8 67 288 178 | 173 -18.677 49.005 25 3 1 4 1 2 4 8 98 280 179 | 174 12.140 30.042 23 19 1 4 1 2 4 8 64 419 180 | 175 3.113 -40.302 23 19 1 4 1 2 4 8 178 496 181 | 176 17.133 37.079 4 4 1 4 1 2 4 8 243 555 182 | 177 22.577 0.836 17 19 1 4 1 2 4 8 127 365 183 | 178 -30.426 52.942 12 15 1 4 1 2 4 8 232 440 184 | 179 33.765 -67.108 11 21 1 4 1 2 4 8 101 405 185 | 180 5.414 1.532 24 25 1 4 1 2 4 8 251 455 186 | 181 72.156 30.103 22 4 1 4 1 2 4 8 74 295 187 | 182 -24.109 -34.625 10 3 1 4 1 2 4 8 177 514 188 | 183 17.413 75.146 10 6 1 4 1 2 4 8 296 624 189 | 184 10.638 -60.052 10 6 1 4 1 2 4 8 207 489 190 | 185 -5.176 -48.999 5 21 1 4 1 2 4 8 255 532 191 | 186 -19.342 21.118 13 25 1 4 1 2 4 8 153 394 192 | 187 -46.802 -14.661 18 10 1 4 1 2 4 8 78 317 193 | 188 -16.779 43.439 25 25 1 4 1 2 4 8 271 576 194 | 189 14.093 -13.605 12 19 1 4 1 2 4 8 249 529 195 | 190 -21.063 26.019 18 19 1 4 1 2 4 8 154 380 196 | 191 39.813 -40.668 9 22 1 4 1 2 4 8 207 393 197 | 192 52.057 30.334 23 5 1 4 1 2 4 8 295 631 198 | 193 -0.140 7.266 0 0 0 0 0 1000 199 | 194 -23.138 48.450 0 0 0 0 0 1000 200 | 195 -26.102 16.809 0 0 0 0 0 1000 201 | 196 0.244 -35.892 0 0 0 0 0 1000 202 | -------------------------------------------------------------------------------- /new_algorithm2.py: -------------------------------------------------------------------------------- 1 | # Dependencies 2 | 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.cluster import KMeans 6 | from sklearn.preprocessing import LabelEncoder 7 | from sklearn.preprocessing import MinMaxScaler 8 | import seaborn as sns 9 | import matplotlib.pyplot as plt 10 | import json, random, math 11 | 12 | def euclideanDistance(x1, y1, x2, y2): 13 | return round(math.sqrt( pow(x2 - x1, 2) + pow(y2 - y1, 2)), 3) 14 | 15 | class Solution: 16 | 17 | def __init__(self, depots, instance, clusters): 18 | self.instance = instance 19 | self.route = [i for i in range(1, instance["number_of_customers"] + 1)] 20 | self.fitness = 0 21 | self.depots = depots 22 | self.nVehicles = instance["number_of_vehicles"] 23 | self.clusters = clusters 24 | random.shuffle(self.route) 25 | 26 | 27 | # def crossover(self, ind1, ind2): 28 | # midpoint = random.choice(range(len(ind2))) 29 | # return ind1[midpoint:] + ind2[:midpoint] 30 | 31 | # def crossover(self, ind1, ind2): 32 | # size = min(len(ind1), len(ind2)) 33 | # # print(len(ind1), len(ind2)) 34 | # cxpoint1, cxpoint2 = sorted(random.sample(range(size), 2)) 35 | # temp1 = ind1[cxpoint1:cxpoint2+1] + ind2 36 | # temp2 = ind1[cxpoint1:cxpoint2+1] + ind1 37 | # ind1 = [] 38 | # for gene in temp1: 39 | # if gene not in ind1: 40 | # ind1.append(gene) 41 | # # return ind1 42 | # ind2 = [] 43 | # for gene in temp2: 44 | # if gene not in ind2: 45 | # ind2.append(gene) 46 | # print(len(ind1), len(ind2)) 47 | # return ind1, ind2 48 | 49 | def crossover(self, ind1, ind2): 50 | cxpoint1, cxpoint2 = sorted(random.sample(range(len(ind1)), 2)) 51 | swath = ind1[cxpoint1:cxpoint2] 52 | 53 | child = [0 for i in range(len(ind1))] 54 | child[cxpoint1:cxpoint2] = swath 55 | #find values from parent 2 that are not in the swath 56 | candidates = [] 57 | for i in range(cxpoint1, cxpoint2): 58 | if ind2[i] not in swath: 59 | candidates.append(ind2[i]) 60 | 61 | for gene in candidates: 62 | indexInParent2 = ind2.index(gene) 63 | valueFromParent1 = ind1[indexInParent2] 64 | indexInParent2 = ind2.index(valueFromParent1) 65 | while child[indexInParent2] != 0: 66 | valueFromParent1 = ind1[indexInParent2] 67 | indexInParent2 = ind2.index(valueFromParent1) 68 | child[indexInParent2] = gene 69 | 70 | for i in range(len(child)): 71 | if child[i] == 0: 72 | child[i] = ind2[i] 73 | 74 | return child 75 | 76 | 77 | 78 | def mutate(self): 79 | 80 | start, stop = sorted(random.sample(range(len(individual)), 2)) 81 | newIndividual = individual[:start] + individual[stop:start-1:-1] + individual[stop+1:] 82 | return newIndividual 83 | 84 | def ind2route(self, clusters): 85 | customersDepot = {} 86 | routes = [] 87 | subRoute = [] 88 | elapsedTime = 0 89 | vehicleLoad = 0 90 | instance = self.instance 91 | individual = self.route 92 | depots = self.depots 93 | 94 | for depot in depots: 95 | customersDepot[depot] = [] 96 | 97 | for depot in depots: 98 | for customerID in individual: 99 | if customerID in clusters[depot]: 100 | customersDepot[depot].append(customerID) 101 | 102 | for depot, customers in customersDepot.items(): 103 | subRoute.append(depot) 104 | initialDepot = instance["depot_%i" % depot] 105 | lastCustomer = depot 106 | maximumTime = initialDepot["max_route_duration"] 107 | maximumCapacity = initialDepot["max_vehicle_load"] 108 | for customer in customers: 109 | actualCustomer = instance["customer_%i" % customer] 110 | distance = instance["distance_matrix"][lastCustomer - 1][customer - 1] 111 | waitTime = max(actualCustomer["ready_time"] - ( elapsedTime + distance), 0) 112 | vehicleLoad += actualCustomer["demand"] 113 | returnTime = instance["distance_matrix"][depot - 1][customer - 1] 114 | updatedElapsedTime = elapsedTime + distance + returnTime + waitTime 115 | #check if elapsed time and vehicle load is less than a fixed amount 116 | if (updatedElapsedTime <= maximumTime and vehicleLoad <= maximumCapacity): 117 | subRoute.append(customer) 118 | lastCustomer = customer 119 | elapsedTime = updatedElapsedTime - returnTime 120 | else: 121 | subRoute.append(depot) 122 | routes.append(subRoute) 123 | updatedElapsedTime = 0 124 | elapsedTime = 0 125 | vehicleLoad = 0 126 | subRoute = [] 127 | subRoute.append(depot) 128 | subRoute.append(customer) 129 | 130 | subRoute.append(depot) 131 | routes.append(subRoute) 132 | subRoute = [] 133 | updatedElapsedTime = 0 134 | elapsedTime = 0 135 | vehicleLoad = 0 136 | return routes 137 | 138 | 139 | def euclideanCost(self, decodedIndividual): 140 | totalCost = 0 141 | routeCost = 0 142 | distance = 0 143 | individual = decodedIndividual 144 | instance = self.instance 145 | depots = self.depots 146 | for routes in individual: 147 | depot = routes[0] 148 | lastCustomer = depot 149 | for index in range(1, len(routes) - 1): 150 | distance = instance["distance_matrix"][lastCustomer - 1][routes[index] - 1] 151 | routeCost += distance 152 | lastCustomer = routes[index] 153 | totalCost += routeCost 154 | routeCost = 0 155 | return totalCost 156 | 157 | 158 | def calculateFitness(self, clusters, fitnessObjective): 159 | routeCost = self.euclideanCost(self.ind2route(clusters)) 160 | fitness = fitnessObjective / routeCost 161 | self.fitness = fitness 162 | 163 | 164 | 165 | def reproduction(population, pool, mutation_rate, clusters): 166 | offspring = [] 167 | for i in range(len(population)): 168 | parentA = random.choice(pool) 169 | parentB = random.choice(pool) 170 | child1 = Solution(parentA.depots, parentA.instance, clusters) 171 | child1.route = parentA.crossover(parentA.route, parentB.route) 172 | offspring.append(child1) 173 | return offspring 174 | 175 | def bestInd(population): 176 | maxFitness = 0 177 | bestInd = 0 178 | for i in range(len(population)): 179 | if population[i].fitness > maxFitness: 180 | maxFitness = population[i].fitness 181 | bestInd = population[i] 182 | return bestInd 183 | 184 | def mating_pool(population): 185 | pool = [] 186 | for i in range(len(population)): 187 | n = int(population[i].fitness * 100) 188 | for j in range(n): 189 | pool.append(population[i]) 190 | return pool 191 | 192 | def clustering(depots, csvInstance, jsonInstance): 193 | newClusters = {} 194 | for depot in depots: 195 | newClusters[depot] = [] 196 | 197 | depotsCoordinates = [ [instance["depot_%i" % depot]["coordinates"]["x"], 198 | instance["depot_%i" % depot]["coordinates"]["y"]] for depot in depots] 199 | x = np.array(csvInstance) 200 | clusters = np.array(depotsCoordinates) 201 | kmeans = KMeans(n_clusters=len(depots), init=clusters, n_init=1).fit(x) 202 | 203 | labels = kmeans.labels_ 204 | for i in range(len(labels)): 205 | if i + 1 not in depots: 206 | newClusters[depots[labels[i]]].append(i + 1) 207 | 208 | return newClusters 209 | 210 | 211 | instance = '' 212 | instanceName = 'pr01' 213 | fitnessObjective = 1083.98 214 | nPop = 100 215 | nGen = 0 216 | with open('data/c-mdvrptw/pr01.txt.json') as json_file: 217 | instance = json.load(json_file) 218 | 219 | number_of_customers = instance["number_of_customers"] 220 | depots = [i for i in range(number_of_customers + 1, number_of_customers + instance["number_of_depots"] + 1)] 221 | customers = [i for i in range(1, number_of_customers + 1)] 222 | 223 | pr01 = pd.read_csv(r'C:\Users\juanj\Documents\Trabajo de Titulo 2\algorithm\pr01_2.csv') 224 | depotsCoordinates = [ [instance["depot_%i" % depot]["coordinates"]["x"], 225 | instance["depot_%i" % depot]["coordinates"]["y"]] for depot in depots] 226 | clusters = clustering(depots, pr01, instance) 227 | # clusters = {49: [7, 9, 31, 32, 35, 36, 37, 41, 42, 44, 46], 50: [3, 6, 10, 11, 22, 27, 34, 45, 48], 228 | # 51: [1, 4, 5, 8, 13, 14, 16, 17, 18, 19, 20, 26, 28, 29, 33], 229 | # 52: [2, 12, 15, 21, 23, 24, 25, 30, 38, 39, 40, 43, 47]} 230 | 231 | population = [] 232 | for i in range(nPop): 233 | population.append(Solution(depots, instance, clusters)) 234 | population[i].calculateFitness(clusters, fitnessObjective) 235 | 236 | while nGen < 75: 237 | print('-- Generation {} --'.format(nGen)) 238 | pool = mating_pool(population) 239 | 240 | offspring = [] 241 | offspring = reproduction(population, pool, 0.02, clusters) 242 | for i in range(len(offspring)): 243 | offspring[i].calculateFitness(clusters, fitnessObjective) 244 | population = offspring 245 | fits = [ind.fitness for ind in population] 246 | length = len(population) 247 | mean = sum(fits) / length 248 | sum2 = sum(x*x for x in fits) 249 | std = abs(sum2 / length - mean**2)**0.5 250 | print(' Min {}'.format(min(fits))) 251 | print(' Max {}'.format(max(fits))) 252 | print(' Avg {}'.format(mean)) 253 | print(' Std {}'.format(std)) 254 | nGen += 1 255 | 256 | for i in range(len(population)): 257 | cost = population[i].euclideanCost(population[i].ind2route(clusters)) 258 | 259 | 260 | bestInd = bestInd(population) 261 | print('\n') 262 | print("Best individual Cost: %s " % bestInd.euclideanCost(bestInd.ind2route(clusters))) 263 | print("Best individual Fitness: %s " % bestInd.fitness) 264 | print("Best individual Route: %s " % bestInd.route) 265 | 266 | json_file.close() 267 | -------------------------------------------------------------------------------- /core/mdvrptw.py: -------------------------------------------------------------------------------- 1 | # Dependencies 2 | 3 | import pandas as pd 4 | import numpy as np 5 | from sklearn.cluster import KMeans 6 | from sklearn.preprocessing import LabelEncoder 7 | from sklearn.preprocessing import MinMaxScaler 8 | import seaborn as sns 9 | import matplotlib.pyplot as plt 10 | import json, random, math, os 11 | 12 | def euclideanDistance(x1, y1, x2, y2): 13 | return round(math.sqrt( pow(x2 - x1, 2) + pow(y2 - y1, 2)), 3) 14 | 15 | class Solution: 16 | 17 | def __init__(self, depots, instance, clusters): 18 | self.instance = instance 19 | self.route = [i for i in range(1, instance["number_of_customers"] + 1)] 20 | self.fitness = 0 21 | self.depots = depots 22 | self.nVehicles = instance["number_of_vehicles"] 23 | self.clusters = clusters 24 | random.shuffle(self.route) 25 | 26 | 27 | # def crossover(self, ind1, ind2): 28 | # midpoint = random.choice(range(len(ind2))) 29 | # return ind1[midpoint:] + ind2[:midpoint] 30 | 31 | # def crossover(self, ind1, ind2): 32 | # size = min(len(ind1), len(ind2)) 33 | # # print(len(ind1), len(ind2)) 34 | # cxpoint1, cxpoint2 = sorted(random.sample(range(size), 2)) 35 | # temp1 = ind1[cxpoint1:cxpoint2+1] + ind2 36 | # temp2 = ind1[cxpoint1:cxpoint2+1] + ind1 37 | # ind1 = [] 38 | # for gene in temp1: 39 | # if gene not in ind1: 40 | # ind1.append(gene) 41 | # # return ind1 42 | # ind2 = [] 43 | # for gene in temp2: 44 | # if gene not in ind2: 45 | # ind2.append(gene) 46 | # print(len(ind1), len(ind2)) 47 | # return ind1, ind2 48 | 49 | def crossover(self, ind1, ind2): 50 | cxpoint1, cxpoint2 = sorted(random.sample(range(len(ind1)), 2)) 51 | swath = ind1[cxpoint1:cxpoint2] 52 | 53 | child = [0 for i in range(len(ind1))] 54 | child[cxpoint1:cxpoint2] = swath 55 | #find values from parent 2 that are not in the swath 56 | candidates = [] 57 | for i in range(cxpoint1, cxpoint2): 58 | if ind2[i] not in swath: 59 | candidates.append(ind2[i]) 60 | 61 | for gene in candidates: 62 | indexInParent2 = ind2.index(gene) 63 | valueFromParent1 = ind1[indexInParent2] 64 | indexInParent2 = ind2.index(valueFromParent1) 65 | while child[indexInParent2] != 0: 66 | valueFromParent1 = ind1[indexInParent2] 67 | indexInParent2 = ind2.index(valueFromParent1) 68 | child[indexInParent2] = gene 69 | 70 | for i in range(len(child)): 71 | if child[i] == 0: 72 | child[i] = ind2[i] 73 | 74 | return child 75 | 76 | 77 | def mutate(self): 78 | 79 | start, stop = sorted(random.sample(range(len(individual)), 2)) 80 | newIndividual = individual[:start] + individual[stop:start-1:-1] + individual[stop+1:] 81 | return newIndividual 82 | 83 | def ind2route(self, clusters): 84 | customersDepot = {} 85 | routes = [] 86 | subRoute = [] 87 | elapsedTime = 0 88 | vehicleLoad = 0 89 | instance = self.instance 90 | individual = self.route 91 | depots = self.depots 92 | 93 | for depot in depots: 94 | customersDepot[depot] = [] 95 | 96 | for depot in depots: 97 | for customerID in individual: 98 | if customerID in clusters[depot]: 99 | customersDepot[depot].append(customerID) 100 | 101 | for depot, customers in customersDepot.items(): 102 | subRoute.append(depot) 103 | initialDepot = instance["depot_%i" % depot] 104 | lastCustomer = depot 105 | maximumTime = initialDepot["max_route_duration"] 106 | maximumCapacity = initialDepot["max_vehicle_load"] 107 | for customer in customers: 108 | actualCustomer = instance["customer_%i" % customer] 109 | distance = instance["distance_matrix"][lastCustomer - 1][customer - 1] 110 | waitTime = max(actualCustomer["ready_time"] - ( elapsedTime + distance), 0) 111 | vehicleLoad += actualCustomer["demand"] 112 | returnTime = instance["distance_matrix"][depot - 1][customer - 1] 113 | updatedElapsedTime = elapsedTime + distance + returnTime + waitTime 114 | #check if elapsed time and vehicle load is less than a fixed amount 115 | if (updatedElapsedTime <= maximumTime and vehicleLoad <= maximumCapacity): 116 | subRoute.append(customer) 117 | lastCustomer = customer 118 | elapsedTime = updatedElapsedTime - returnTime 119 | else: 120 | subRoute.append(depot) 121 | routes.append(subRoute) 122 | updatedElapsedTime = 0 123 | elapsedTime = 0 124 | vehicleLoad = 0 125 | subRoute = [] 126 | subRoute.append(depot) 127 | subRoute.append(customer) 128 | 129 | subRoute.append(depot) 130 | routes.append(subRoute) 131 | subRoute = [] 132 | updatedElapsedTime = 0 133 | elapsedTime = 0 134 | vehicleLoad = 0 135 | return routes 136 | 137 | 138 | def euclideanCost(self, decodedIndividual): 139 | totalCost = 0 140 | routeCost = 0 141 | distance = 0 142 | individual = decodedIndividual 143 | instance = self.instance 144 | depots = self.depots 145 | for routes in individual: 146 | depot = routes[0] 147 | lastCustomer = depot 148 | for index in range(1, len(routes) - 1): 149 | distance = instance["distance_matrix"][lastCustomer - 1][routes[index] - 1] 150 | routeCost += distance 151 | lastCustomer = routes[index] 152 | totalCost += routeCost 153 | routeCost = 0 154 | return totalCost 155 | 156 | 157 | def calculateFitness(self, clusters, fitnessObjective): 158 | routeCost = self.euclideanCost(self.ind2route(clusters)) 159 | fitness = fitnessObjective / routeCost 160 | self.fitness = fitness 161 | 162 | 163 | 164 | def reproduction(population, pool, mutation_rate, clusters): 165 | offspring = [] 166 | for i in range(len(population)): 167 | parentA = random.choice(pool) 168 | parentB = random.choice(pool) 169 | child1 = Solution(parentA.depots, parentA.instance, clusters) 170 | child1.route = parentA.crossover(parentA.route, parentB.route) 171 | offspring.append(child1) 172 | return offspring 173 | 174 | def bestIndividual(population): 175 | maxFitness = 0 176 | bestInd = 0 177 | for i in range(len(population)): 178 | if population[i].fitness > maxFitness: 179 | maxFitness = population[i].fitness 180 | bestInd = population[i] 181 | return bestInd 182 | 183 | def mating_pool(population): 184 | pool = [] 185 | for i in range(len(population)): 186 | n = int(population[i].fitness * 100) 187 | for j in range(n): 188 | pool.append(population[i]) 189 | return pool 190 | 191 | def clustering(depots, csvInstance, jsonInstance): 192 | newClusters = {} 193 | for depot in depots: 194 | newClusters[depot] = [] 195 | 196 | depotsCoordinates = [ [jsonInstance["depot_%i" % depot]["coordinates"]["x"], 197 | jsonInstance["depot_%i" % depot]["coordinates"]["y"]] for depot in depots] 198 | x = np.array(csvInstance) 199 | clusters = np.array(depotsCoordinates) 200 | kmeans = KMeans(n_clusters=len(depots), init=clusters, n_init=1).fit(x) 201 | 202 | labels = kmeans.labels_ 203 | for i in range(len(labels)): 204 | if i + 1 not in depots: 205 | newClusters[depots[labels[i]]].append(i + 1) 206 | 207 | return newClusters 208 | 209 | def run_mdvrptw(instance_name, unit_cost, init_cost, wait_cost, delay_cost, ind_size, 210 | pop_size, cx_pb, mut_pb, n_gen, export_csv): 211 | 212 | instance = '' 213 | fitnessObjective = 1763.07 214 | nPop = pop_size 215 | nGen = 1 216 | 217 | with open('data/c-mdvrptw/json/%s' % instance_name) as json_file: 218 | instance = json.load(json_file) 219 | 220 | number_of_customers = instance["number_of_customers"] 221 | depots = [i for i in range(number_of_customers + 1, number_of_customers + instance["number_of_depots"] + 1)] 222 | customers = [i for i in range(1, number_of_customers + 1)] 223 | 224 | # pr01 = pd.read_csv(r'C:\Users\juanj\Documents\Trabajo de Titulo 2\algorithm\pr01_2.csv') 225 | 226 | csvPath = r'data/c-mdvrptw/csv/%s.csv' % instance_name.split(".")[0] 227 | pr01 = pd.read_csv(csvPath) 228 | depotsCoordinates = [ [instance["depot_%i" % depot]["coordinates"]["x"], 229 | instance["depot_%i" % depot]["coordinates"]["y"]] for depot in depots] 230 | clusters = clustering(depots, pr01, instance) 231 | # clusters = {49: [7, 9, 31, 32, 35, 36, 37, 41, 42, 44, 46], 50: [3, 6, 10, 11, 22, 27, 34, 45, 48], 232 | # 51: [1, 4, 5, 8, 13, 14, 16, 17, 18, 19, 20, 26, 28, 29, 33], 233 | # 52: [2, 12, 15, 21, 23, 24, 25, 30, 38, 39, 40, 43, 47]} 234 | 235 | #population initialization 236 | population = [] 237 | for i in range(nPop): 238 | population.append(Solution(depots, instance, clusters)) 239 | population[i].calculateFitness(clusters, fitnessObjective) 240 | 241 | #Generational bucle 242 | while nGen < n_gen: 243 | print('-- Generation {} --'.format(nGen)) 244 | pool = mating_pool(population) 245 | 246 | offspring = [] 247 | offspring = reproduction(population, pool, mut_pb, clusters) 248 | 249 | for i in range(len(offspring)): 250 | offspring[i].calculateFitness(clusters, fitnessObjective) 251 | 252 | #replace old population with offspring 253 | population = offspring 254 | 255 | #stats 256 | fits = [ind.fitness for ind in population] 257 | length = len(population) 258 | mean = sum(fits) / length 259 | sum2 = sum(x*x for x in fits) 260 | std = abs(sum2 / length - mean**2)**0.5 261 | print(' Min {}'.format(min(fits))) 262 | print(' Max {}'.format(max(fits))) 263 | print(' Avg {}'.format(mean)) 264 | print(' Std {}'.format(std)) 265 | nGen += 1 266 | 267 | print('-- End of evolution --') 268 | for i in range(len(population)): 269 | cost = population[i].euclideanCost(population[i].ind2route(clusters)) 270 | 271 | 272 | bestInd = bestIndividual(population) 273 | print('\n') 274 | print("Best individual Cost: %s " % bestInd.euclideanCost(bestInd.ind2route(clusters))) 275 | print("Best individual Fitness: %s " % bestInd.fitness) 276 | print("Best individual Route: %s " % bestInd.route) 277 | 278 | json_file.close() 279 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr05.txt: -------------------------------------------------------------------------------- 1 | 6 6 240 4 2 | 420 180 3 | 420 180 4 | 420 180 5 | 420 180 6 | 1 65.991 -49.829 20 10 1 4 1 2 4 8 422 555 7 | 2 -36.938 -36.743 20 25 1 4 1 2 4 8 128 256 8 | 3 -2.734 18.774 13 18 1 4 1 2 4 8 122 271 9 | 4 31.116 -35.907 13 16 1 4 1 2 4 8 141 286 10 | 5 2.789 8.008 17 14 1 4 1 2 4 8 106 199 11 | 6 31.152 43.665 24 7 1 4 1 2 4 8 92 212 12 | 7 -36.304 -21.307 15 9 1 4 1 2 4 8 345 487 13 | 8 52.814 35.046 12 16 1 4 1 2 4 8 366 470 14 | 9 11.267 20.660 20 24 1 4 1 2 4 8 127 225 15 | 10 -0.623 23.114 20 6 1 4 1 2 4 8 179 292 16 | 11 43.091 -12.360 3 22 1 4 1 2 4 8 308 401 17 | 12 6.854 19.073 8 11 1 4 1 2 4 8 139 234 18 | 13 -6.848 25.317 16 19 1 4 1 2 4 8 306 406 19 | 14 -27.930 -23.712 20 20 1 4 1 2 4 8 465 566 20 | 15 33.240 -30.487 21 20 1 4 1 2 4 8 422 523 21 | 16 63.885 -52.881 23 16 1 4 1 2 4 8 305 404 22 | 17 -13.672 -43.945 21 5 1 4 1 2 4 8 151 301 23 | 18 -0.647 -27.502 3 21 1 4 1 2 4 8 455 587 24 | 19 59.406 -49.261 19 19 1 4 1 2 4 8 107 233 25 | 20 27.167 -5.713 20 18 1 4 1 2 4 8 221 316 26 | 21 70.001 -20.844 7 22 1 4 1 2 4 8 329 454 27 | 22 35.327 -65.167 14 19 1 4 1 2 4 8 384 490 28 | 23 -14.386 -42.236 24 15 1 4 1 2 4 8 79 175 29 | 24 19.366 30.273 3 24 1 4 1 2 4 8 76 173 30 | 25 -39.789 10.101 13 20 1 4 1 2 4 8 297 433 31 | 26 -15.753 -40.204 1 17 1 4 1 2 4 8 358 514 32 | 27 -28.418 -40.503 5 20 1 4 1 2 4 8 375 521 33 | 28 27.081 9.735 2 12 1 4 1 2 4 8 218 364 34 | 29 22.205 -9.216 9 23 1 4 1 2 4 8 87 235 35 | 30 -0.830 -6.525 18 15 1 4 1 2 4 8 289 439 36 | 31 10.217 75.531 5 19 1 4 1 2 4 8 432 603 37 | 32 7.446 18.500 12 23 1 4 1 2 4 8 433 581 38 | 33 48.914 -20.294 11 9 1 4 1 2 4 8 360 450 39 | 34 -18.958 -29.675 17 21 1 4 1 2 4 8 194 333 40 | 35 16.815 -6.659 21 17 1 4 1 2 4 8 373 503 41 | 36 2.509 -30.420 5 6 1 4 1 2 4 8 429 536 42 | 37 15.424 4.913 5 10 1 4 1 2 4 8 298 473 43 | 38 60.315 89.801 7 9 1 4 1 2 4 8 445 557 44 | 39 40.405 -45.367 24 19 1 4 1 2 4 8 134 248 45 | 40 -26.837 -54.272 1 17 1 4 1 2 4 8 267 395 46 | 41 69.171 72.577 12 22 1 4 1 2 4 8 76 254 47 | 42 -22.815 46.173 6 8 1 4 1 2 4 8 281 426 48 | 43 -14.258 -30.249 5 25 1 4 1 2 4 8 232 409 49 | 44 76.050 12.073 19 13 1 4 1 2 4 8 317 492 50 | 45 63.043 60.022 23 1 1 4 1 2 4 8 333 424 51 | 46 -20.239 -81.970 8 15 1 4 1 2 4 8 474 608 52 | 47 -41.168 -42.023 1 16 1 4 1 2 4 8 364 478 53 | 48 79.199 -29.016 20 20 1 4 1 2 4 8 329 467 54 | 49 -20.728 -7.068 16 14 1 4 1 2 4 8 330 429 55 | 50 -4.163 -53.497 8 2 1 4 1 2 4 8 478 585 56 | 51 39.594 -40.460 16 5 1 4 1 2 4 8 66 239 57 | 52 -47.125 -77.850 25 16 1 4 1 2 4 8 126 239 58 | 53 -1.233 49.182 5 12 1 4 1 2 4 8 264 403 59 | 54 -0.372 -27.264 10 1 1 4 1 2 4 8 144 253 60 | 55 31.537 -8.722 24 15 1 4 1 2 4 8 154 325 61 | 56 -0.134 -20.264 16 5 1 4 1 2 4 8 244 419 62 | 57 66.394 -26.691 7 9 1 4 1 2 4 8 159 273 63 | 58 6.104 -64.093 14 8 1 4 1 2 4 8 130 263 64 | 59 13.544 -20.874 11 2 1 4 1 2 4 8 334 434 65 | 60 54.211 -6.537 15 10 1 4 1 2 4 8 420 514 66 | 61 22.046 -34.137 23 25 1 4 1 2 4 8 392 544 67 | 62 -9.723 4.706 5 24 1 4 1 2 4 8 151 316 68 | 63 -11.584 -46.857 16 7 1 4 1 2 4 8 117 240 69 | 64 -2.905 -0.305 13 14 1 4 1 2 4 8 131 298 70 | 65 13.837 -44.452 18 3 1 4 1 2 4 8 64 237 71 | 66 17.480 27.747 14 23 1 4 1 2 4 8 119 209 72 | 67 2.972 -43.689 3 21 1 4 1 2 4 8 109 268 73 | 68 -44.354 -38.202 22 13 1 4 1 2 4 8 340 441 74 | 69 -59.399 1.514 16 15 1 4 1 2 4 8 356 476 75 | 70 -13.452 -75.531 16 14 1 4 1 2 4 8 391 556 76 | 71 -5.518 -51.947 2 7 1 4 1 2 4 8 295 430 77 | 72 -9.589 -26.880 19 20 1 4 1 2 4 8 139 232 78 | 73 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10.242 12 25 1 4 1 2 4 8 132 489 236 | 231 16.254 -22.583 17 15 1 4 1 2 4 8 216 536 237 | 232 -36.639 -34.033 18 3 1 4 1 2 4 8 154 461 238 | 233 -27.356 -50.177 5 1 1 4 1 2 4 8 99 307 239 | 234 -53.705 11.334 16 1 1 4 1 2 4 8 166 420 240 | 235 26.276 -5.994 9 20 1 4 1 2 4 8 68 259 241 | 236 -31.519 34.589 6 19 1 4 1 2 4 8 251 560 242 | 237 -14.612 -31.506 1 2 1 4 1 2 4 8 225 467 243 | 238 -4.242 -30.865 4 9 1 4 1 2 4 8 134 451 244 | 239 -18.524 -29.486 20 4 1 4 1 2 4 8 66 334 245 | 240 -17.133 -19.397 19 16 1 4 1 2 4 8 232 421 246 | 241 34.430 -17.151 0 0 0 0 0 1000 247 | 242 0.269 -8.154 0 0 0 0 0 1000 248 | 243 3.140 13.297 0 0 0 0 0 1000 249 | 244 -3.113 -29.745 0 0 0 0 0 1000 250 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr09.txt: -------------------------------------------------------------------------------- 1 | 6 4 216 6 2 | 450 180 3 | 450 180 4 | 450 180 5 | 450 180 6 | 450 180 7 | 450 180 8 | 1 -41.235 -66.357 14 3 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29.517 21 23 1 6 1 2 4 8 16 32 476 597 156 | 149 -43.628 -32.318 24 9 1 6 1 2 4 8 16 32 456 631 157 | 150 -16.602 -29.327 4 1 1 6 1 2 4 8 16 32 320 458 158 | 151 -29.810 -12.408 18 12 1 6 1 2 4 8 16 32 391 503 159 | 152 -51.910 -28.711 10 17 1 6 1 2 4 8 16 32 171 340 160 | 153 -1.801 -18.671 3 2 1 6 1 2 4 8 16 32 432 582 161 | 154 18.738 21.399 4 5 1 6 1 2 4 8 16 32 242 384 162 | 155 60.120 7.788 25 1 1 6 1 2 4 8 16 32 141 241 163 | 156 43.079 42.017 3 20 1 6 1 2 4 8 16 32 214 358 164 | 157 -39.435 -5.902 22 19 1 6 1 2 4 8 16 32 63 175 165 | 158 -35.889 -16.034 19 13 1 6 1 2 4 8 16 32 62 206 166 | 159 -41.827 38.995 20 10 1 6 1 2 4 8 16 32 130 252 167 | 160 84.497 29.822 7 9 1 6 1 2 4 8 16 32 145 249 168 | 161 -17.560 -10.480 19 14 1 6 1 2 4 8 16 32 263 423 169 | 162 -48.383 -48.920 19 4 1 6 1 2 4 8 16 32 285 419 170 | 163 -49.335 -17.969 22 20 1 6 1 2 4 8 16 32 268 447 171 | 164 16.095 -2.490 4 17 1 6 1 2 4 8 16 32 277 370 172 | 165 65.527 14.026 25 19 1 6 1 2 4 8 16 32 114 218 173 | 166 -2.930 13.220 23 19 1 6 1 2 4 8 16 32 310 476 174 | 167 -18.768 -23.663 24 16 1 6 1 2 4 8 16 32 215 326 175 | 168 -91.095 -80.737 25 4 1 6 1 2 4 8 16 32 384 497 176 | 169 -21.088 -13.293 4 1 1 6 1 2 4 8 16 32 278 420 177 | 170 46.985 17.224 3 21 1 6 1 2 4 8 16 32 132 224 178 | 171 -61.957 -33.038 7 9 1 6 1 2 4 8 16 32 132 307 179 | 172 -9.003 -25.452 10 2 1 6 1 2 4 8 16 32 234 404 180 | 173 33.661 -7.666 10 24 1 6 1 2 4 8 16 32 251 426 181 | 174 -25.592 -10.840 23 1 1 6 1 2 4 8 16 32 118 226 182 | 175 36.560 -2.441 18 21 1 6 1 2 4 8 16 32 335 509 183 | 176 7.141 24.817 8 13 1 6 1 2 4 8 16 32 275 404 184 | 177 -43.170 -30.487 16 15 1 6 1 2 4 8 16 32 408 507 185 | 178 -21.228 0.751 4 4 1 6 1 2 4 8 16 32 92 192 186 | 179 19.696 10.767 14 10 1 6 1 2 4 8 16 32 258 360 187 | 180 -6.494 -29.425 23 19 1 6 1 2 4 8 16 32 383 484 188 | 181 54.388 8.630 17 1 1 6 1 2 4 8 16 32 418 591 189 | 182 64.203 77.612 7 25 1 6 1 2 4 8 16 32 256 420 190 | 183 40.381 1.611 8 3 1 6 1 2 4 8 16 32 103 256 191 | 184 -41.711 -5.133 14 21 1 6 1 2 4 8 16 32 438 532 192 | 185 74.274 -19.922 22 7 1 6 1 2 4 8 16 32 337 457 193 | 186 -73.999 -54.626 7 18 1 6 1 2 4 8 16 32 172 287 194 | 187 29.706 15.454 16 3 1 6 1 2 4 8 16 32 122 291 195 | 188 23.785 12.177 24 1 1 6 1 2 4 8 16 32 231 340 196 | 189 42.578 45.325 15 2 1 6 1 2 4 8 16 32 183 291 197 | 190 -16.730 -16.559 15 8 1 6 1 2 4 8 16 32 424 589 198 | 191 -14.697 -0.116 5 10 1 6 1 2 4 8 16 32 474 642 199 | 192 -19.769 8.392 9 19 1 6 1 2 4 8 16 32 358 486 200 | 193 -30.267 14.893 24 17 1 6 1 2 4 8 16 32 80 173 201 | 194 -63.116 -67.462 6 19 1 6 1 2 4 8 16 32 230 367 202 | 195 86.029 38.916 3 24 1 6 1 2 4 8 16 32 409 543 203 | 196 79.480 -24.866 24 24 1 6 1 2 4 8 16 32 333 431 204 | 197 35.144 14.734 22 6 1 6 1 2 4 8 16 32 152 301 205 | 198 -49.365 -9.052 2 2 1 6 1 2 4 8 16 32 256 373 206 | 199 -49.475 -17.578 21 7 1 6 1 2 4 8 16 32 120 276 207 | 200 57.501 16.327 17 10 1 6 1 2 4 8 16 32 225 389 208 | 201 54.889 12.024 13 13 1 6 1 2 4 8 16 32 140 289 209 | 202 -53.027 -33.179 21 20 1 6 1 2 4 8 16 32 130 235 210 | 203 -0.415 16.644 3 1 1 6 1 2 4 8 16 32 235 342 211 | 204 21.594 16.144 9 17 1 6 1 2 4 8 16 32 345 436 212 | 205 -46.924 -49.261 14 17 1 6 1 2 4 8 16 32 449 608 213 | 206 18.457 19.733 9 16 1 6 1 2 4 8 16 32 309 404 214 | 207 35.822 19.763 3 9 1 6 1 2 4 8 16 32 211 336 215 | 208 71.869 29.034 22 25 1 6 1 2 4 8 16 32 252 361 216 | 209 -56.378 -42.755 13 1 1 6 1 2 4 8 16 32 405 510 217 | 210 -25.800 25.159 10 23 1 6 1 2 4 8 16 32 326 484 218 | 211 -5.676 33.850 7 10 1 6 1 2 4 8 16 32 238 413 219 | 212 54.041 -5.292 3 15 1 6 1 2 4 8 16 32 444 570 220 | 213 -63.074 27.380 11 25 1 6 1 2 4 8 16 32 452 587 221 | 214 -16.864 -49.768 17 25 1 6 1 2 4 8 16 32 131 273 222 | 215 47.919 13.629 15 16 1 6 1 2 4 8 16 32 368 516 223 | 216 -10.724 -2.075 4 1 1 6 1 2 4 8 16 32 412 518 224 | 217 -15.067 -23.950 0 0 0 0 0 1000 225 | 218 38.791 22.443 0 0 0 0 0 1000 226 | 219 -41.833 -35.361 0 0 0 0 0 1000 227 | 220 -29.523 -4.843 0 0 0 0 0 1000 228 | 221 49.725 11.990 0 0 0 0 0 1000 229 | 222 21.133 14.142 0 0 0 0 0 1000 230 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr19.txt: -------------------------------------------------------------------------------- 1 | 6 3 216 6 2 | 450 180 3 | 450 180 4 | 450 180 5 | 450 180 6 | 450 180 7 | 450 180 8 | 1 -41.235 -66.357 14 3 1 6 1 2 4 8 16 32 114 369 9 | 2 34.064 -59.357 16 16 1 6 1 2 4 8 16 32 212 565 10 | 3 20.917 -52.582 17 8 1 6 1 2 4 8 16 32 65 409 11 | 4 -38.538 -37.396 19 15 1 6 1 2 4 8 16 32 87 343 12 | 5 41.058 22.931 6 12 1 6 1 2 4 8 16 32 265 624 13 | 6 -54.034 -53.131 7 13 1 6 1 2 4 8 16 32 149 440 14 | 7 8.099 80.725 3 8 1 6 1 2 4 8 16 32 254 485 15 | 8 81.299 13.409 10 24 1 6 1 2 4 8 16 32 235 543 16 | 9 14.459 -2.972 4 14 1 6 1 2 4 8 16 32 190 485 17 | 10 -52.539 60.107 17 17 1 6 1 2 4 8 16 32 224 455 18 | 11 -57.202 -34.229 10 8 1 6 1 2 4 8 16 32 172 470 19 | 12 -65.552 -40.222 13 11 1 6 1 2 4 8 16 32 266 573 20 | 13 -19.751 -10.278 20 8 1 6 1 2 4 8 16 32 175 446 21 | 14 -26.965 -87.012 6 17 1 6 1 2 4 8 16 32 299 588 22 | 15 3.192 -88.654 18 19 1 6 1 2 4 8 16 32 203 457 23 | 16 34.521 32.837 3 13 1 6 1 2 4 8 16 32 74 352 24 | 17 80.035 5.273 10 2 1 6 1 2 4 8 16 32 139 475 25 | 18 -39.838 -67.731 4 8 1 6 1 2 4 8 16 32 77 385 26 | 19 14.600 11.365 15 21 1 6 1 2 4 8 16 32 91 444 27 | 20 -20.990 -80.511 4 13 1 6 1 2 4 8 16 32 246 570 28 | 21 48.242 47.284 6 17 1 6 1 2 4 8 16 32 243 582 29 | 22 59.338 -31.860 19 4 1 6 1 2 4 8 16 32 230 558 30 | 23 -13.995 -45.624 7 22 1 6 1 2 4 8 16 32 297 649 31 | 24 32.397 -32.532 20 24 1 6 1 2 4 8 16 32 243 429 32 | 25 2.826 50.824 12 11 1 6 1 2 4 8 16 32 134 364 33 | 26 -66.077 -34.686 8 6 1 6 1 2 4 8 16 32 271 587 34 | 27 -13.660 18.640 18 19 1 6 1 2 4 8 16 32 109 356 35 | 28 -59.344 12.769 1 11 1 6 1 2 4 8 16 32 273 495 36 | 29 -5.133 -18.463 25 23 1 6 1 2 4 8 16 32 217 547 37 | 30 -22.961 -21.729 16 4 1 6 1 2 4 8 16 32 105 341 38 | 31 58.130 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159 -41.827 38.995 20 10 1 6 1 2 4 8 16 32 95 419 167 | 160 84.497 29.822 7 9 1 6 1 2 4 8 16 32 253 504 168 | 161 -17.560 -10.480 19 14 1 6 1 2 4 8 16 32 62 353 169 | 162 -48.383 -48.920 19 4 1 6 1 2 4 8 16 32 188 438 170 | 163 -49.335 -17.969 22 20 1 6 1 2 4 8 16 32 184 437 171 | 164 16.095 -2.490 4 17 1 6 1 2 4 8 16 32 85 299 172 | 165 65.527 14.026 25 19 1 6 1 2 4 8 16 32 224 521 173 | 166 -2.930 13.220 23 19 1 6 1 2 4 8 16 32 163 482 174 | 167 -18.768 -23.663 24 16 1 6 1 2 4 8 16 32 130 330 175 | 168 -91.095 -80.737 25 4 1 6 1 2 4 8 16 32 253 597 176 | 169 -21.088 -13.293 4 1 1 6 1 2 4 8 16 32 136 435 177 | 170 46.985 17.224 3 21 1 6 1 2 4 8 16 32 90 369 178 | 171 -61.957 -33.038 7 9 1 6 1 2 4 8 16 32 197 550 179 | 172 -9.003 -25.452 10 2 1 6 1 2 4 8 16 32 72 261 180 | 173 33.661 -7.666 10 24 1 6 1 2 4 8 16 32 162 488 181 | 174 -25.592 -10.840 23 1 1 6 1 2 4 8 16 32 232 589 182 | 175 36.560 -2.441 18 21 1 6 1 2 4 8 16 32 275 596 183 | 176 7.141 24.817 8 13 1 6 1 2 4 8 16 32 73 373 184 | 177 -43.170 -30.487 16 15 1 6 1 2 4 8 16 32 297 653 185 | 178 -21.228 0.751 4 4 1 6 1 2 4 8 16 32 145 357 186 | 179 19.696 10.767 14 10 1 6 1 2 4 8 16 32 207 479 187 | 180 -6.494 -29.425 23 19 1 6 1 2 4 8 16 32 94 292 188 | 181 54.388 8.630 17 1 1 6 1 2 4 8 16 32 285 560 189 | 182 64.203 77.612 7 25 1 6 1 2 4 8 16 32 289 589 190 | 183 40.381 1.611 8 3 1 6 1 2 4 8 16 32 186 440 191 | 184 -41.711 -5.133 14 21 1 6 1 2 4 8 16 32 97 374 192 | 185 74.274 -19.922 22 7 1 6 1 2 4 8 16 32 72 425 193 | 186 -73.999 -54.626 7 18 1 6 1 2 4 8 16 32 134 484 194 | 187 29.706 15.454 16 3 1 6 1 2 4 8 16 32 63 274 195 | 188 23.785 12.177 24 1 1 6 1 2 4 8 16 32 125 379 196 | 189 42.578 45.325 15 2 1 6 1 2 4 8 16 32 113 375 197 | 190 -16.730 -16.559 15 8 1 6 1 2 4 8 16 32 196 518 198 | 191 -14.697 -0.116 5 10 1 6 1 2 4 8 16 32 169 505 199 | 192 -19.769 8.392 9 19 1 6 1 2 4 8 16 32 94 408 200 | 193 -30.267 14.893 24 17 1 6 1 2 4 8 16 32 80 305 201 | 194 -63.116 -67.462 6 19 1 6 1 2 4 8 16 32 83 363 202 | 195 86.029 38.916 3 24 1 6 1 2 4 8 16 32 73 396 203 | 196 79.480 -24.866 24 24 1 6 1 2 4 8 16 32 172 407 204 | 197 35.144 14.734 22 6 1 6 1 2 4 8 16 32 66 415 205 | 198 -49.365 -9.052 2 2 1 6 1 2 4 8 16 32 229 458 206 | 199 -49.475 -17.578 21 7 1 6 1 2 4 8 16 32 71 283 207 | 200 57.501 16.327 17 10 1 6 1 2 4 8 16 32 271 576 208 | 201 54.889 12.024 13 13 1 6 1 2 4 8 16 32 200 490 209 | 202 -53.027 -33.179 21 20 1 6 1 2 4 8 16 32 120 416 210 | 203 -0.415 16.644 3 1 1 6 1 2 4 8 16 32 125 448 211 | 204 21.594 16.144 9 17 1 6 1 2 4 8 16 32 124 348 212 | 205 -46.924 -49.261 14 17 1 6 1 2 4 8 16 32 109 448 213 | 206 18.457 19.733 9 16 1 6 1 2 4 8 16 32 209 554 214 | 207 35.822 19.763 3 9 1 6 1 2 4 8 16 32 89 341 215 | 208 71.869 29.034 22 25 1 6 1 2 4 8 16 32 160 497 216 | 209 -56.378 -42.755 13 1 1 6 1 2 4 8 16 32 147 471 217 | 210 -25.800 25.159 10 23 1 6 1 2 4 8 16 32 81 375 218 | 211 -5.676 33.850 7 10 1 6 1 2 4 8 16 32 267 463 219 | 212 54.041 -5.292 3 15 1 6 1 2 4 8 16 32 80 389 220 | 213 -63.074 27.380 11 25 1 6 1 2 4 8 16 32 85 377 221 | 214 -16.864 -49.768 17 25 1 6 1 2 4 8 16 32 69 293 222 | 215 47.919 13.629 15 16 1 6 1 2 4 8 16 32 141 410 223 | 216 -10.724 -2.075 4 1 1 6 1 2 4 8 16 32 151 461 224 | 217 -15.067 -23.950 0 0 0 0 0 1000 225 | 218 38.791 22.443 0 0 0 0 0 1000 226 | 219 -41.833 -35.361 0 0 0 0 0 1000 227 | 220 -29.523 -4.843 0 0 0 0 0 1000 228 | 221 49.725 11.990 0 0 0 0 0 1000 229 | 222 21.133 14.142 0 0 0 0 0 1000 230 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr06.txt: -------------------------------------------------------------------------------- 1 | 6 7 288 4 2 | 400 175 3 | 400 175 4 | 400 175 5 | 400 175 6 | 1 -66.174 -37.811 13 19 1 4 1 2 4 8 218 308 7 | 2 2.673 -35.223 16 21 1 4 1 2 4 8 129 244 8 | 3 38.751 8.618 1 17 1 4 1 2 4 8 475 593 9 | 4 62.653 20.667 9 6 1 4 1 2 4 8 230 341 10 | 5 60.974 -6.110 20 6 1 4 1 2 4 8 159 330 11 | 6 -98.535 -39.532 2 19 1 4 1 2 4 8 390 561 12 | 7 8.411 -81.274 5 17 1 4 1 2 4 8 389 538 13 | 8 -14.569 26.056 10 19 1 4 1 2 4 8 89 260 14 | 9 -34.186 -76.697 10 2 1 4 1 2 4 8 454 628 15 | 10 55.145 -33.911 25 3 1 4 1 2 4 8 306 441 16 | 11 15.869 8.716 2 24 1 4 1 2 4 8 391 565 17 | 12 -63.416 -20.282 5 5 1 4 1 2 4 8 106 254 18 | 13 -9.357 -42.212 16 9 1 4 1 2 4 8 460 613 19 | 14 -53.619 3.729 24 20 1 4 1 2 4 8 76 240 20 | 15 53.491 -3.308 7 19 1 4 1 2 4 8 366 523 21 | 16 -22.614 -32.794 19 20 1 4 1 2 4 8 270 377 22 | 17 39.612 -1.550 15 4 1 4 1 2 4 8 78 235 23 | 18 16.705 -22.748 7 3 1 4 1 2 4 8 251 385 24 | 19 -51.245 -13.757 16 2 1 4 1 2 4 8 475 597 25 | 20 -29.907 14.380 20 23 1 4 1 2 4 8 229 340 26 | 21 -70.441 -27.911 21 6 1 4 1 2 4 8 203 342 27 | 22 -75.385 -30.811 7 17 1 4 1 2 4 8 388 521 28 | 23 10.590 -28.931 17 17 1 4 1 2 4 8 425 544 29 | 24 86.340 -45.935 11 3 1 4 1 2 4 8 333 503 30 | 25 3.864 34.583 6 16 1 4 1 2 4 8 394 535 31 | 26 -37.598 -42.114 4 15 1 4 1 2 4 8 380 503 32 | 27 69.440 1.379 16 23 1 4 1 2 4 8 160 318 33 | 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407 75 | 70 58.093 67.352 21 25 1 4 1 2 4 8 444 586 76 | 71 90.015 9.485 16 4 1 4 1 2 4 8 424 553 77 | 72 -6.622 15.216 15 4 1 4 1 2 4 8 269 441 78 | 73 -2.875 -42.841 23 13 1 4 1 2 4 8 351 468 79 | 74 -37.714 -7.306 22 18 1 4 1 2 4 8 453 562 80 | 75 44.171 -11.224 21 9 1 4 1 2 4 8 322 501 81 | 76 -58.704 -2.808 21 5 1 4 1 2 4 8 476 625 82 | 77 -3.912 10.071 21 12 1 4 1 2 4 8 390 518 83 | 78 -41.370 -1.495 7 22 1 4 1 2 4 8 125 254 84 | 79 58.179 27.563 15 9 1 4 1 2 4 8 90 192 85 | 80 -63.861 26.147 15 11 1 4 1 2 4 8 244 411 86 | 81 -56.793 -15.918 16 6 1 4 1 2 4 8 169 287 87 | 82 22.217 -27.509 13 19 1 4 1 2 4 8 132 238 88 | 83 -1.031 -14.838 22 10 1 4 1 2 4 8 371 472 89 | 84 -57.703 -10.480 1 1 1 4 1 2 4 8 221 377 90 | 85 80.847 6.299 22 10 1 4 1 2 4 8 159 285 91 | 86 2.887 10.822 24 20 1 4 1 2 4 8 440 588 92 | 87 -37.390 -7.800 6 18 1 4 1 2 4 8 174 344 93 | 88 -33.600 -26.740 5 1 1 4 1 2 4 8 365 530 94 | 89 95.197 24.231 14 11 1 4 1 2 4 8 414 549 95 | 90 -13.904 29.327 19 20 1 4 1 2 4 8 381 523 96 | 91 -53.461 9.296 16 12 1 4 1 2 4 8 79 206 97 | 92 67.578 13.007 20 17 1 4 1 2 4 8 304 417 98 | 93 -83.881 -53.265 12 16 1 4 1 2 4 8 76 226 99 | 94 30.255 -72.430 7 3 1 4 1 2 4 8 140 277 100 | 95 -22.333 -20.538 23 14 1 4 1 2 4 8 146 240 101 | 96 3.741 -35.461 9 12 1 4 1 2 4 8 274 433 102 | 97 -20.093 -43.982 19 9 1 4 1 2 4 8 400 569 103 | 98 -47.687 -21.100 17 1 1 4 1 2 4 8 301 465 104 | 99 -20.447 -13.849 1 7 1 4 1 2 4 8 76 222 105 | 100 34.515 3.967 25 7 1 4 1 2 4 8 108 274 106 | 101 20.514 -11.884 12 5 1 4 1 2 4 8 372 522 107 | 102 -46.039 -22.565 15 9 1 4 1 2 4 8 290 430 108 | 103 -39.630 25.385 16 15 1 4 1 2 4 8 368 503 109 | 104 -1.025 50.433 10 4 1 4 1 2 4 8 76 207 110 | 105 -28.632 23.486 24 10 1 4 1 2 4 8 471 608 111 | 106 -52.997 -16.248 24 21 1 4 1 2 4 8 335 459 112 | 107 -3.345 -22.955 18 2 1 4 1 2 4 8 281 416 113 | 108 -62.640 -26.349 21 6 1 4 1 2 4 8 185 358 114 | 109 25.403 0.531 21 1 1 4 1 2 4 8 285 401 115 | 110 -29.895 72.919 17 23 1 4 1 2 4 8 225 333 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297 | 292 3.690 -26.099 0 0 0 0 0 1000 298 | -------------------------------------------------------------------------------- /data/c-mdvrptw/txt/pr16.txt: -------------------------------------------------------------------------------- 1 | 6 6 288 4 2 | 400 175 3 | 400 175 4 | 400 175 5 | 400 175 6 | 1 -66.174 -37.811 13 19 1 4 1 2 4 8 94 327 7 | 2 2.673 -35.223 16 21 1 4 1 2 4 8 206 531 8 | 3 38.751 8.618 1 17 1 4 1 2 4 8 294 651 9 | 4 62.653 20.667 9 6 1 4 1 2 4 8 261 563 10 | 5 60.974 -6.110 20 6 1 4 1 2 4 8 85 406 11 | 6 -98.535 -39.532 2 19 1 4 1 2 4 8 87 309 12 | 7 8.411 -81.274 5 17 1 4 1 2 4 8 104 439 13 | 8 -14.569 26.056 10 19 1 4 1 2 4 8 226 449 14 | 9 -34.186 -76.697 10 2 1 4 1 2 4 8 270 551 15 | 10 55.145 -33.911 25 3 1 4 1 2 4 8 268 599 16 | 11 15.869 8.716 2 24 1 4 1 2 4 8 127 351 17 | 12 -63.416 -20.282 5 5 1 4 1 2 4 8 139 340 18 | 13 -9.357 -42.212 16 9 1 4 1 2 4 8 126 481 19 | 14 -53.619 3.729 24 20 1 4 1 2 4 8 192 453 20 | 15 53.491 -3.308 7 19 1 4 1 2 4 8 155 467 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4 8 194 531 165 | 160 1.978 17.517 20 9 1 4 1 2 4 8 251 581 166 | 161 -80.566 -38.196 10 2 1 4 1 2 4 8 269 574 167 | 162 -49.927 -36.713 24 2 1 4 1 2 4 8 128 447 168 | 163 -60.449 -27.820 6 4 1 4 1 2 4 8 147 442 169 | 164 -78.748 -44.226 18 15 1 4 1 2 4 8 140 452 170 | 165 54.260 -9.259 15 11 1 4 1 2 4 8 133 346 171 | 166 -42.059 -26.636 6 15 1 4 1 2 4 8 262 554 172 | 167 -57.336 -16.718 10 15 1 4 1 2 4 8 156 483 173 | 168 -1.276 -13.330 7 16 1 4 1 2 4 8 248 561 174 | 169 -24.158 39.197 24 14 1 4 1 2 4 8 201 405 175 | 170 17.908 -40.442 3 25 1 4 1 2 4 8 133 342 176 | 171 59.833 -3.674 22 9 1 4 1 2 4 8 91 276 177 | 172 -3.510 6.256 1 23 1 4 1 2 4 8 188 446 178 | 173 40.167 8.594 4 16 1 4 1 2 4 8 114 332 179 | 174 -1.691 -54.376 12 10 1 4 1 2 4 8 286 552 180 | 175 -36.035 -17.065 14 4 1 4 1 2 4 8 202 521 181 | 176 7.275 -4.144 17 25 1 4 1 2 4 8 201 420 182 | 177 30.219 -6.830 20 20 1 4 1 2 4 8 110 467 183 | 178 -26.379 -63.055 19 5 1 4 1 2 4 8 90 434 184 | 179 28.772 -0.031 18 19 1 4 1 2 4 8 247 453 185 | 180 2.625 5.072 22 2 1 4 1 2 4 8 271 563 186 | 181 3.595 -21.600 14 13 1 4 1 2 4 8 67 347 187 | 182 -64.948 -10.956 14 19 1 4 1 2 4 8 265 593 188 | 183 18.671 -41.010 20 2 1 4 1 2 4 8 148 412 189 | 184 29.291 12.799 16 16 1 4 1 2 4 8 299 580 190 | 185 4.529 26.654 25 2 1 4 1 2 4 8 89 295 191 | 186 25.360 29.718 12 6 1 4 1 2 4 8 208 431 192 | 187 57.349 -15.405 1 3 1 4 1 2 4 8 254 576 193 | 188 -11.566 -19.012 11 20 1 4 1 2 4 8 114 362 194 | 189 -45.441 -71.808 18 13 1 4 1 2 4 8 215 491 195 | 190 2.197 -3.564 8 9 1 4 1 2 4 8 224 474 196 | 191 -1.855 -24.475 8 5 1 4 1 2 4 8 258 482 197 | 192 -59.851 -48.938 1 15 1 4 1 2 4 8 65 396 198 | 193 70.081 -17.816 23 22 1 4 1 2 4 8 218 458 199 | 194 -4.932 28.754 19 18 1 4 1 2 4 8 231 429 200 | 195 -46.625 -23.529 18 21 1 4 1 2 4 8 119 354 201 | 196 -49.872 -35.339 16 16 1 4 1 2 4 8 256 610 202 | 197 -67.499 8.142 7 22 1 4 1 2 4 8 216 437 203 | 198 31.195 -18.243 13 23 1 4 1 2 4 8 247 443 204 | 199 -42.139 -52.704 5 1 1 4 1 2 4 8 125 469 205 | 200 -15.094 18.933 3 1 1 4 1 2 4 8 118 436 206 | 201 -78.625 -24.341 22 13 1 4 1 2 4 8 173 527 207 | 202 51.471 -9.607 21 14 1 4 1 2 4 8 243 438 208 | 203 29.987 -31.061 18 18 1 4 1 2 4 8 69 331 209 | 204 46.741 1.453 14 5 1 4 1 2 4 8 223 544 210 | 205 -52.954 31.738 19 24 1 4 1 2 4 8 207 452 211 | 206 10.535 -61.432 18 11 1 4 1 2 4 8 83 424 212 | 207 -15.948 -56.012 12 5 1 4 1 2 4 8 210 507 213 | 208 -7.776 -54.730 21 1 1 4 1 2 4 8 112 400 214 | 209 11.047 -20.905 15 21 1 4 1 2 4 8 274 462 215 | 210 -24.359 -23.047 8 6 1 4 1 2 4 8 282 511 216 | 211 2.948 -20.398 5 6 1 4 1 2 4 8 158 391 217 | 212 -82.654 24.323 3 5 1 4 1 2 4 8 294 503 218 | 213 -12.134 42.065 2 23 1 4 1 2 4 8 177 389 219 | 214 -98.035 -36.359 5 16 1 4 1 2 4 8 202 511 220 | 215 -61.102 -13.477 3 18 1 4 1 2 4 8 247 584 221 | 216 -39.136 6.287 12 10 1 4 1 2 4 8 256 615 222 | 217 41.180 7.971 4 9 1 4 1 2 4 8 142 324 223 | 218 4.254 19.830 23 18 1 4 1 2 4 8 189 390 224 | 219 -34.143 -37.915 8 22 1 4 1 2 4 8 122 312 225 | 220 -4.559 23.566 7 24 1 4 1 2 4 8 156 388 226 | 221 37.891 -1.947 10 15 1 4 1 2 4 8 296 530 227 | 222 78.259 15.936 18 6 1 4 1 2 4 8 197 427 228 | 223 -8.661 -25.110 4 12 1 4 1 2 4 8 293 489 229 | 224 10.071 37.164 9 19 1 4 1 2 4 8 214 552 230 | 225 -42.621 -46.985 19 19 1 4 1 2 4 8 221 515 231 | 226 92.157 -94.183 5 17 1 4 1 2 4 8 178 416 232 | 227 32.202 -39.844 19 22 1 4 1 2 4 8 182 363 233 | 228 -55.231 -15.027 7 25 1 4 1 2 4 8 188 380 234 | 229 -26.746 -21.490 22 11 1 4 1 2 4 8 286 576 235 | 230 -75.513 -0.708 2 23 1 4 1 2 4 8 84 440 236 | 231 34.021 12.665 7 10 1 4 1 2 4 8 98 421 237 | 232 -2.643 -20.831 4 17 1 4 1 2 4 8 146 389 238 | 233 53.369 13.770 11 6 1 4 1 2 4 8 191 532 239 | 234 -14.948 -40.259 12 20 1 4 1 2 4 8 204 513 240 | 235 67.828 5.750 2 6 1 4 1 2 4 8 182 424 241 | 236 -13.220 -26.031 13 4 1 4 1 2 4 8 117 476 242 | 237 4.169 -32.617 22 25 1 4 1 2 4 8 231 437 243 | 238 -26.050 -33.313 6 7 1 4 1 2 4 8 142 464 244 | 239 31.964 -45.703 11 11 1 4 1 2 4 8 159 402 245 | 240 96.820 26.208 22 14 1 4 1 2 4 8 84 357 246 | 241 -44.696 -37.854 9 14 1 4 1 2 4 8 262 593 247 | 242 33.417 11.792 21 15 1 4 1 2 4 8 296 499 248 | 243 -35.187 19.226 8 2 1 4 1 2 4 8 151 340 249 | 244 -14.233 -74.988 4 14 1 4 1 2 4 8 62 246 250 | 245 37.158 -0.836 19 17 1 4 1 2 4 8 186 535 251 | 246 -51.697 -26.447 20 24 1 4 1 2 4 8 292 492 252 | 247 23.535 5.896 22 10 1 4 1 2 4 8 124 456 253 | 248 -18.433 53.711 10 20 1 4 1 2 4 8 262 515 254 | 249 -11.340 -17.621 20 11 1 4 1 2 4 8 296 561 255 | 250 -7.812 -35.895 10 16 1 4 1 2 4 8 165 389 256 | 251 3.522 26.941 23 3 1 4 1 2 4 8 160 481 257 | 252 -46.588 -22.266 5 11 1 4 1 2 4 8 151 414 258 | 253 -2.997 -0.549 25 10 1 4 1 2 4 8 266 613 259 | 254 32.147 17.151 7 18 1 4 1 2 4 8 178 475 260 | 255 32.019 13.007 16 20 1 4 1 2 4 8 87 441 261 | 256 -6.628 29.889 15 24 1 4 1 2 4 8 241 569 262 | 257 77.777 -25.397 9 16 1 4 1 2 4 8 146 437 263 | 258 30.707 65.704 19 2 1 4 1 2 4 8 255 528 264 | 259 -43.811 -43.036 11 14 1 4 1 2 4 8 168 483 265 | 260 49.561 16.827 19 10 1 4 1 2 4 8 125 454 266 | 261 -28.931 -0.439 10 22 1 4 1 2 4 8 258 583 267 | 262 40.363 16.382 20 14 1 4 1 2 4 8 225 462 268 | 263 22.748 40.674 17 15 1 4 1 2 4 8 194 536 269 | 264 -1.471 6.567 22 7 1 4 1 2 4 8 204 423 270 | 265 -1.190 16.864 24 20 1 4 1 2 4 8 216 541 271 | 266 8.032 41.821 5 1 1 4 1 2 4 8 153 335 272 | 267 -44.019 -22.784 5 24 1 4 1 2 4 8 111 393 273 | 268 12.366 -11.353 17 22 1 4 1 2 4 8 148 460 274 | 269 33.276 15.881 7 23 1 4 1 2 4 8 193 535 275 | 270 -34.790 -15.045 25 20 1 4 1 2 4 8 285 475 276 | 271 -96.857 -19.391 12 15 1 4 1 2 4 8 256 467 277 | 272 38.434 -5.353 11 14 1 4 1 2 4 8 83 366 278 | 273 -25.391 0.220 6 12 1 4 1 2 4 8 122 328 279 | 274 0.848 -31.860 13 4 1 4 1 2 4 8 118 351 280 | 275 -24.023 95.691 23 15 1 4 1 2 4 8 238 445 281 | 276 -4.272 35.468 15 12 1 4 1 2 4 8 184 499 282 | 277 -6.927 1.855 13 8 1 4 1 2 4 8 189 379 283 | 278 -70.953 -3.217 10 2 1 4 1 2 4 8 206 524 284 | 279 -32.648 -16.522 24 24 1 4 1 2 4 8 78 359 285 | 280 42.859 -21.680 13 5 1 4 1 2 4 8 131 363 286 | 281 5.182 6.403 12 8 1 4 1 2 4 8 212 425 287 | 282 36.920 -4.987 21 21 1 4 1 2 4 8 212 448 288 | 283 49.591 27.783 10 5 1 4 1 2 4 8 232 519 289 | 284 45.038 10.785 12 16 1 4 1 2 4 8 79 315 290 | 285 7.690 14.056 9 8 1 4 1 2 4 8 70 343 291 | 286 -60.907 18.640 5 24 1 4 1 2 4 8 167 374 292 | 287 -64.496 8.923 18 23 1 4 1 2 4 8 268 617 293 | 288 37.158 -14.990 24 6 1 4 1 2 4 8 291 475 294 | 289 2.277 7.840 0 0 0 0 0 1000 295 | 290 32.883 -1.779 0 0 0 0 0 1000 296 | 291 -49.554 -17.828 0 0 0 0 0 1000 297 | 292 3.690 -26.099 0 0 0 0 0 1000 298 | --------------------------------------------------------------------------------