├── industrialucn ├── experimental │ ├── __init__.py │ └── osrm.py ├── __init__.py ├── queueing.py ├── econstraint.py └── econstraint_test.py ├── Coordinates.xlsx ├── README.md ├── test.py ├── setup.py └── LICENSE /industrialucn/experimental/__init__.py: -------------------------------------------------------------------------------- 1 | from . import osrm 2 | -------------------------------------------------------------------------------- /industrialucn/__init__.py: -------------------------------------------------------------------------------- 1 | from industrialucn import experimental 2 | -------------------------------------------------------------------------------- /Coordinates.xlsx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/industrial-ucn/orlab-code/master/Coordinates.xlsx -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # IndustrialUCN 2 | 3 | IndustrialUCN is an open-source effort from the Industrial Engineering Community at Universidad Católica del Norte, 4 | Chile ([industrial.ucn.cl](https://industrial.ucn.cl)). This package is intended for students 5 | and researchers of our field. 6 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import pandas as pd 2 | from geopy.distance import great_circle 3 | 4 | from orlabucn import osrm 5 | from orlabucn.osrm import Location, osrm_time_matrix 6 | 7 | df = pd.read_excel('Puntos_medios.xlsx') 8 | ref_lat, ref_lon = df.Lat.min(), df.Lon.min() 9 | locations = [] 10 | for i, row in df.iterrows(): 11 | x = great_circle((ref_lat, ref_lon), (ref_lat, row.Lon)).meters 12 | y = great_circle((ref_lat, ref_lon), (row.Lat, ref_lon)).meters 13 | new_location = Location(id=int(row.Id), lat=row.Lat, lon=row.Lon, x=x, y=y) 14 | locations.append(new_location) 15 | 16 | pairs = [(l1, l2) for l1 in locations for l2 in locations 17 | if l1 != l2 and abs(l1.x - l2.x) <= 1_000 and abs(l1.y - l2.y) <= 1_000] 18 | 19 | print(len(locations) * (len(locations) - 1), "->", len(pairs)) 20 | 21 | osrm.data_path = 'osrm5.csv' 22 | 23 | osrm_time_matrix(pairs) 24 | -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r") as fh: 4 | long_description = fh.read() 5 | 6 | setuptools.setup( 7 | name="industrialucn", 8 | version="0.2.0", 9 | author="Hernan Caceres", 10 | author_email="hcaceres@ucn.cl", 11 | description="Generic tool for numerical experiments", 12 | long_description=long_description, 13 | long_description_content_type="text/markdown", 14 | url="https://industrial.ucn.cl", 15 | packages=setuptools.find_packages(), 16 | classifiers=[ 17 | "Development Status :: 3 - Alpha", 18 | "Intended Audience :: Science/Research", 19 | "License :: Free for non-commercial use", 20 | "Operating System :: OS Independent", 21 | "Programming Language :: Python :: 3.7", 22 | "Topic :: Scientific/Engineering" 23 | ], 24 | python_requires='>=3.7', 25 | ) 26 | -------------------------------------------------------------------------------- /industrialucn/experimental/osrm.py: -------------------------------------------------------------------------------- 1 | import os.path 2 | from collections import namedtuple 3 | 4 | import numpy as np 5 | import pandas as pd 6 | import requests 7 | 8 | Location = namedtuple('Location', 'id lat lon x y') 9 | 10 | data_path = 'osrm.csv' 11 | 12 | 13 | def osrm_time_row(locations): 14 | assert len(locations) <= 8_000, "Limit over maximum allowed." 15 | assert len(locations) >= 2, "More than one location is needed." 16 | str_locations = ';'.join(['%0.6f,%0.6f' % (loc.lon, loc.lat) for loc in locations]) 17 | r = requests.get('http://localhost:5000/table/v1/driving/' + str_locations + '?sources=0') 18 | return r.json()['durations'][0][1:] 19 | 20 | 21 | def osrm_time_matrix_batch_query(location_pair_list, query_limit=4_000): 22 | assert query_limit <= 8_000, "Limit over maximum allowed." 23 | assert query_limit >= 1, "Limit must be positive." 24 | assert query_limit == int(query_limit), " Limit must be integer." 25 | assert len(location_pair_list) > 0, "Not enough requests." 26 | location_pair_list = sorted(location_pair_list) 27 | origin = None 28 | destinations = [] 29 | n_pairs = len(location_pair_list) 30 | progress_print = None 31 | for k, (loc1, loc2) in enumerate(location_pair_list): 32 | # check if origin changed 33 | if origin != loc1: 34 | if len(destinations) > 0: 35 | res = osrm_time_row([origin] + destinations) 36 | save_response(origin, destinations, res) 37 | origin = loc1 38 | destinations = [loc2] 39 | else: 40 | destinations.append(loc2) 41 | # check if the query limit is reached or end of location pair list 42 | if len(destinations) == query_limit or k + 1 == len(location_pair_list): 43 | res = osrm_time_row([origin] + destinations) 44 | save_response(origin, destinations, res) 45 | origin = None 46 | destinations = [] 47 | progress = round((k + 1) / n_pairs * 100) 48 | if progress != progress_print: 49 | progress_print = progress 50 | print(f'{progress_print}%') 51 | return None 52 | 53 | 54 | def save_response(origin, destinations, response): 55 | n = len(destinations) 56 | i_list = [origin.id for _ in range(n)] 57 | j_list = [destination.id for destination in destinations] 58 | df = pd.DataFrame({'i': i_list, 'j': j_list, 'response': response}) 59 | with open(data_path, 'a') as f: 60 | df.to_csv(f, header=False, index=False) 61 | return None 62 | 63 | 64 | def osrm_time_matrix(location_pair_list): 65 | if not os.path.isfile(data_path): 66 | osrm_time_matrix_batch_query(location_pair_list) 67 | else: 68 | df1 = pd.DataFrame({'i': [i.id for i, _ in location_pair_list], 69 | 'j': [j.id for _, j in location_pair_list]}) 70 | df2 = pd.read_csv(data_path, header=None, usecols=[0, 1], names=['i', 'j'], 71 | dtype={'i': np.int32, 'j': np.int32}) 72 | df_all = df1.merge(df2.drop_duplicates(), on=['i', 'j'], how='left', indicator=True) 73 | df_empties = df_all[df_all['_merge'] == 'left_only'] 74 | locations = {} 75 | for l1, l2 in location_pair_list: 76 | locations[l1.id] = l1 77 | locations[l2.id] = l2 78 | new_location_pair_list = [] 79 | for k, row in df_empties.iterrows(): 80 | new_location_pair_list.append((locations[row.i], locations[row.j])) 81 | if len(new_location_pair_list) > 0: 82 | osrm_time_matrix_batch_query(new_location_pair_list) 83 | return None 84 | -------------------------------------------------------------------------------- /industrialucn/queueing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def mm1(arrival, departure): 5 | rho = arrival / departure 6 | po = 1 - rho 7 | nqueue = rho ** 2 / (1 - rho) 8 | nsis = nqueue + rho 9 | tqueue = nqueue / arrival 10 | tsis = nsis / arrival 11 | 12 | return rho, po, nqueue, nsis, tqueue, tsis 13 | 14 | 15 | def mm1prob(arrival, departure, j): 16 | rho = arrival / departure 17 | po = 1 - rho 18 | pj = rho ** j * po 19 | 20 | return po, pj 21 | 22 | 23 | def mms(arrival, departure, s): 24 | rho = arrival / (s * departure) 25 | 26 | po = 1 / (sum(1 / np.math.factorial(i) * (arrival / departure) ** i for i in range(s)) + ( 27 | arrival / departure) ** s / (np.math.factorial(s) * (1 - rho))) 28 | 29 | nqueue = po * (arrival / departure) ** s * rho / (np.math.factorial(s) * (1 - rho) ** 2) 30 | nsis = nqueue + arrival / departure 31 | tqueue = nqueue / arrival 32 | tsis = nsis / arrival 33 | 34 | return rho, po, nqueue, nsis, tqueue, tsis 35 | 36 | 37 | def mmsprob(arrival, departure, s, j): 38 | rho = arrival / (s * departure) 39 | 40 | po = 1 / (sum(1 / np.math.factorial(i) * (arrival / departure) ** i for i in range(s)) + ( 41 | arrival / departure) ** s / (np.math.factorial(s) * (1 - rho))) 42 | 43 | if j <= s: 44 | pj = po * (arrival / departure) ** j / (np.math.factorial(j)) 45 | else: 46 | pj = po * s ** s * (arrival / (s * departure)) ** j / (np.math.factorial(s)) 47 | 48 | return po, pj 49 | 50 | 51 | def mm1k(arrival, departure, k): 52 | rho = arrival / departure 53 | 54 | po = (1 - rho) / (1 - rho ** (k + 1)) 55 | pk = rho ** k * po 56 | lambda_ef = arrival * (1 - pk) 57 | 58 | nqueue = rho / (1 - rho) - (rho + k * rho ** (k + 1)) / (1 - rho ** (k + 1)) 59 | nsis = nqueue + lambda_ef / departure 60 | tqueue = nqueue / lambda_ef 61 | tsis = nsis / lambda_ef 62 | 63 | return rho, po, pk, lambda_ef, nqueue, nsis, tqueue, tsis 64 | 65 | 66 | def mm1kprob(arrival, departure, k, j): 67 | rho = arrival / departure 68 | 69 | po = (1 - rho) / (1 - rho ** (k + 1)) 70 | pj = rho ** j * po 71 | 72 | return po, pj 73 | 74 | 75 | def mmsk(arrival, departure, s, k): 76 | rho = arrival / (s * departure) 77 | 78 | po = 1 / (sum(1 / np.math.factorial(i) * (arrival / departure) ** i for i in range(s)) + ( 79 | arrival / departure) ** s / (np.math.factorial(s)) * (1 - rho ** (k - s + 1)) / (1 - rho)) 80 | 81 | if k <= s: 82 | pk = po * (arrival / departure) ** k / (np.math.factorial(k)) 83 | else: 84 | pk = po * s ** s * (arrival / (s * departure)) ** k / (np.math.factorial(s)) 85 | 86 | lambda_ef = arrival * (1 - pk) 87 | 88 | nqueue = po * (arrival / departure) ** s * rho / (np.math.factorial(s) * (1 - rho) ** 2) * ( 89 | 1 - rho ** (k - s) - (k - s) * rho ** (k - s) * (1 - rho)) 90 | nsis = nqueue + lambda_ef / departure 91 | tqueue = nqueue / lambda_ef 92 | tsis = nsis / lambda_ef 93 | 94 | return rho, po, pk, lambda_ef, nqueue, nsis, tqueue, tsis 95 | 96 | 97 | def mmskprob(arrival, departure, s, k, j): 98 | rho = arrival / (s * departure) 99 | 100 | po = 1 / (sum(1 / np.math.factorial(i) * (arrival / departure) ** i for i in range(s)) + ( 101 | arrival / departure) ** s / (np.math.factorial(s)) * (1 - rho ** (k - s + 1)) / (1 - rho)) 102 | 103 | if j <= s: 104 | pj = po * (arrival / departure) ** j / (np.math.factorial(j)) 105 | else: 106 | pj = po * s ** s * (arrival / (s * departure)) ** j / (np.math.factorial(s)) 107 | 108 | return po, pj 109 | 110 | 111 | def mm1n(arrival, departure, n): 112 | rho = arrival / departure 113 | po = 1 / ( 114 | sum(np.math.factorial(n) / np.math.factorial(n - i) * (arrival / (departure * n)) ** i for i in range(n + 1))) 115 | 116 | nsis = sum(i * np.math.factorial(n) / np.math.factorial(n - i) * (arrival / (departure * n)) ** i * po for i in 117 | range(n + 1)) 118 | nqueue = nsis - arrival * (1 - po) / departure 119 | 120 | lambda_ef = arrival * (n - nsis) 121 | tqueue = nqueue / lambda_ef 122 | tsis = nsis / lambda_ef 123 | 124 | return rho, po, lambda_ef, nqueue, nsis, tqueue, tsis 125 | 126 | 127 | def mm1nprob(arrival, departure, n, j): 128 | po = 1 / ( 129 | sum(np.math.factorial(n) / np.math.factorial(n - i) * (arrival / (departure * n)) ** i for i in range(n + 1))) 130 | 131 | pj = np.math.factorial(n) / np.math.factorial(n - j) * (arrival / (departure * n)) ** j * po 132 | 133 | return po, pj 134 | 135 | 136 | def mmsn(arrival, departure, s, n): 137 | rho = arrival / (s * departure) 138 | po = 1 / (sum( 139 | np.math.factorial(n) / (np.math.factorial(n - i) * np.math.factorial(i)) * (arrival / (departure * n)) ** i for 140 | i in range(0, s)) + s ** s / np.math.factorial(s) * sum( 141 | np.math.factorial(n) / np.math.factorial(n - i) * (arrival / (s * departure * n)) ** i for i in 142 | range(s, n + 1))) 143 | 144 | nsis = po * (arrival / departure * (1 + (arrival / (departure * n))) ** (n - 1) + sum( 145 | np.math.factorial(n) / np.math.factorial(n - j) * j * ( 146 | s ** s / np.math.factorial(s) - s ** j / np.math.factorial(j)) * (arrival / (s * departure * n)) ** j 147 | for j in range(s, n + 1))) 148 | 149 | lambda_ef = arrival * (1 - nsis / n) 150 | nqueue = nsis - (arrival / departure) * (lambda_ef / arrival) 151 | tqueue = nqueue / lambda_ef 152 | tsis = nsis / lambda_ef 153 | 154 | return rho, po, lambda_ef, nqueue, nsis, tqueue, tsis 155 | 156 | 157 | def mmsnprob(arrival, departure, s, n, j): 158 | po = 1 / (sum( 159 | np.math.factorial(n) / (np.math.factorial(n - i) * np.math.factorial(i)) * (arrival / (departure * n)) ** i for 160 | i in range(0, s)) + s ** s / np.math.factorial(s) * sum( 161 | np.math.factorial(n) / np.math.factorial(n - i) * (arrival / (s * departure * n)) ** i for i in 162 | range(s, n + 1))) 163 | 164 | if j <= s: 165 | pj = np.math.factorial(n) / (np.math.factorial(n - j) * np.math.factorial(j)) * ( 166 | arrival / (departure * n)) ** j * po 167 | else: 168 | pj = np.math.factorial(n) / (np.math.factorial(n - j)) * (s ** s / np.math.factorial(s)) * ( 169 | arrival / (s * departure * n)) ** j * po 170 | 171 | return po, pj 172 | -------------------------------------------------------------------------------- /industrialucn/econstraint.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import Union, List, Dict, Callable, NoReturn 3 | 4 | import numpy as np 5 | from docplex.mp.linear import LinearExpr 6 | from docplex.mp.model import Model as CplexModel 7 | from gurobipy import GRB, quicksum, LinExpr 8 | from gurobipy import Model as GurobiModel 9 | 10 | logger = logging.getLogger('econstraint') 11 | 12 | 13 | def _get_payoff_table_gurobi(mdl: GurobiModel, objectives: List[LinExpr]): 14 | payoff_table = {} 15 | p = len(objectives) 16 | for k in range(p): 17 | logger.info(f'Entering loop, k={k}') 18 | mdl.setObjective(objectives[k], sense=GRB.MAXIMIZE) 19 | mdl.optimize() 20 | payoff_table[k, k] = mdl.ObjVal 21 | # noinspection PyArgumentList 22 | constraints = [mdl.addConstr(objectives[k] >= payoff_table[k, k])] 23 | for h in range(p): 24 | logger.info(f'Entering loop, h={h}') 25 | if h != k: 26 | mdl.setObjective(objectives[h], sense=GRB.MAXIMIZE) 27 | mdl.optimize() 28 | payoff_table[k, h] = mdl.ObjVal 29 | # noinspection PyArgumentList 30 | constraints.append( 31 | mdl.addConstr(objectives[h] >= payoff_table[k, h]) 32 | ) 33 | logger.info(f'About to remove constraints, k={k}') 34 | mdl.remove(constraints) 35 | return payoff_table 36 | 37 | 38 | def _get_payoff_table_cplex(mdl: CplexModel, objectives: List[LinearExpr]): 39 | payoff_table = {} 40 | p = len(objectives) 41 | for k in range(p): 42 | logger.info(f'Entering loop, k={k}') 43 | mdl.maximize(objectives[k]) 44 | solution = mdl.solve() 45 | payoff_table[k, k] = solution.get_objective_value() 46 | constraints = [mdl.add_constraint(objectives[k] >= payoff_table[k, k])] 47 | for h in range(p): 48 | logger.info(f'Entering loop, h={h}') 49 | if h != k: 50 | mdl.maximize(objectives[h]) 51 | solution = mdl.solve() 52 | payoff_table[k, h] = solution.get_objective_value() 53 | constraints.append( 54 | mdl.add_constraint(objectives[h] >= payoff_table[k, h]) 55 | ) 56 | logger.info(f'About to remove constraints, k={k}') 57 | mdl.remove(constraints) 58 | return payoff_table 59 | 60 | 61 | def _run_econstraint_gurobi(mdl: GurobiModel, objectives: List[LinExpr], payoff_table, g: Dict[int, int] = None, 62 | solution_extractor: Callable = None) -> NoReturn: 63 | p = len(objectives) 64 | s = mdl.addVars([k for k in range(1, p)], name='s') 65 | lb = {k: min(payoff_table[h, k] for h in range(p)) 66 | for k in range(1, p)} 67 | r = {k: max(payoff_table[h, k] for h in range(p)) - min(payoff_table[h, k] for h in range(p)) 68 | for k in range(1, p)} 69 | logger.debug('r: %s', r) 70 | epsilon = 1e-3 71 | mdl.setObjective(objectives[0] + epsilon * quicksum(s[k] / r[k] for k in range(1, p)), sense=GRB.MAXIMIZE) 72 | if g is None: 73 | g = {k: 3 for k in range(1, p)} # default number of grid 74 | logger.warning('Using default grid g=3') 75 | i = {k: 0 for k in range(1, p)} 76 | while True: 77 | e = {k: lb[k] + (i[k] * r[k]) / g[k] for k in range(1, p)} 78 | logger.info(f'i: {i}, e: {e}') 79 | constraints = mdl.addConstrs(objectives[k] - s[k] == e[k] for k in range(1, p)) 80 | mdl.optimize() 81 | if mdl.SolCount > 0: 82 | solution_extractor() 83 | logger.info('feasible') 84 | else: 85 | logger.info('not feasible') 86 | mdl.remove(constraints) 87 | if all(i[k] == g[k] for k in range(1, p)): 88 | break 89 | else: 90 | for k in range(1, p): 91 | if i[k] == g[k]: 92 | i[k] = 0 93 | else: 94 | i[k] += 1 95 | break 96 | 97 | 98 | def _run_econstraint_cplex(mdl: CplexModel, objectives: List[LinearExpr], payoff_table, g: Dict[int, int] = None, 99 | solution_extractor: Callable = None) -> NoReturn: 100 | p = len(objectives) 101 | s = mdl.continuous_var_dict([k for k in range(1, p)], name='s') 102 | lb = {k: min(payoff_table[h, k] for h in range(p)) 103 | for k in range(1, p)} 104 | r = {k: max(payoff_table[h, k] for h in range(p)) - min(payoff_table[h, k] for h in range(p)) 105 | for k in range(1, p)} 106 | logger.debug('r: %s', r) 107 | epsilon = 1e-3 108 | mdl.maximize(objectives[0] + epsilon * mdl.sum(s[k] / r[k] for k in range(1, p))) 109 | if g is None: 110 | g = {k: 3 for k in range(1, p)} # default number of grid 111 | logger.warning('Using default grid g=3') 112 | i = {k: 0 for k in range(1, p)} 113 | while True: 114 | e = {k: lb[k] + (i[k] * r[k]) / g[k] for k in range(1, p)} 115 | logger.info(f'i: {i}, e: {e}') 116 | constraints = mdl.add_constraints(objectives[k] - s[k] == e[k] for k in range(1, p)) 117 | solution = mdl.solve() 118 | if solution is not None: 119 | solution_extractor() 120 | logger.info('feasible') 121 | else: 122 | logger.info('not feasible') 123 | mdl.remove(constraints) 124 | if all(i[k] == g[k] for k in range(1, p)): 125 | break 126 | else: 127 | for k in range(1, p): 128 | if i[k] == g[k]: 129 | i[k] = 0 130 | else: 131 | i[k] += 1 132 | break 133 | 134 | 135 | def get_payoff_table(model: Union[CplexModel, GurobiModel], 136 | objectives: Union[List[LinearExpr], List[LinExpr]], 137 | optimizer: str): 138 | if optimizer == 'cplex': 139 | return _get_payoff_table_cplex(model, objectives) 140 | elif optimizer == 'gurobi': 141 | return _get_payoff_table_gurobi(model, objectives) 142 | else: 143 | raise NotImplementedError(f'{optimizer} is not a valid optimizer; must be "cplex" or "gurobi"') 144 | 145 | 146 | def run_econstraint(model: Union[CplexModel, GurobiModel], 147 | objectives: Union[List[LinearExpr], List[LinExpr]], 148 | solution_extractor: Callable, 149 | optimizer: str, 150 | g: Dict[int, int] = None, 151 | payoff_table=None) -> NoReturn: 152 | if optimizer == 'cplex': 153 | pot = _get_payoff_table_cplex(model, objectives) if payoff_table is None else payoff_table 154 | logger.debug(pot) 155 | return _run_econstraint_cplex(model, objectives, pot, g, solution_extractor=solution_extractor) 156 | elif optimizer == 'gurobi': 157 | pot = _get_payoff_table_gurobi(model, objectives) if payoff_table is None else payoff_table 158 | logger.debug(pot) 159 | return _run_econstraint_gurobi(model, objectives, pot, g, solution_extractor=solution_extractor) 160 | else: 161 | raise NotImplementedError(f'{optimizer} is not a valid optimizer; must be "cplex" or "gurobi"') 162 | 163 | 164 | def all_non_dominated(solution_list): 165 | for i, fi in enumerate(solution_list): 166 | if any(all(fjk < fik or np.isclose(fjk, fik) for fik, fjk in zip(fi, fj)) and 167 | any(fjk < fik and not np.isclose(fjk, fik) for fik, fjk in zip(fi, fj)) 168 | for j, fj in enumerate(solution_list) if j != i): 169 | return False 170 | return True 171 | 172 | 173 | def all_weakly_non_dominated(solution_list): 174 | for i, fi in enumerate(solution_list): 175 | if any(all(fjk < fik and not np.isclose(fjk, fik) for fik, fjk in zip(fi, fj)) 176 | for j, fj in enumerate(solution_list) if j != i): 177 | return False 178 | return True 179 | -------------------------------------------------------------------------------- /industrialucn/econstraint_test.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import sys 3 | import unittest 4 | 5 | import numpy as np 6 | 7 | from industrialucn import econstraint as ec 8 | 9 | 10 | def config_logger(logger, level): 11 | logger.setLevel(level) 12 | handler = logging.StreamHandler(sys.stdout) 13 | handler.setLevel(logging.DEBUG) 14 | formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') 15 | handler.setFormatter(formatter) 16 | logger.addHandler(handler) 17 | 18 | 19 | config_logger(ec.logger, logging.DEBUG) 20 | 21 | # define parameters 22 | n = 15 # number of parks 23 | m = 50 # number of buildings 24 | rnd = np.random 25 | rnd.seed(0) 26 | cpx = rnd.rand(n) * 300 # x coordinate for parks 27 | cpy = rnd.rand(n) * 100 # y coordinate for parks 28 | cbx = rnd.rand(m) * 300 # x coordinate for buildings 29 | cby = rnd.rand(m) * 100 # y coordinate for buildings 30 | parks = [i for i in range(n)] 31 | buildings = [j for j in range(m)] 32 | distance = {(i, j): np.hypot(cbx[i] - cpx[j], cby[i] - cpy[j]) 33 | for i in buildings for j in parks} 34 | 35 | 36 | class EConstraint(unittest.TestCase): 37 | 38 | def test_dominance(self): 39 | v1 = [[1.0, 4324.8, 165.4], 40 | [2.0, 2757.5, 116.1], 41 | [3.0, 2106.9, 74.8], 42 | [13.0, 1351.1, 56.9], 43 | [2.0, 2757.5, 116.1], 44 | [2.0, 2966.0, 107.7], 45 | [3.0, 2106.9, 74.8], 46 | [13.0, 1351.1, 56.9]] 47 | 48 | v2 = [[1.0, 4324.8, 165.4], 49 | [2.0, 2757.5, 116.1], 50 | [3.0, 2106.9, 74.8], 51 | [13.0, 1351.1, 56.9], 52 | [2.0, 2757.5, 116.1], 53 | [2.0, 2966.0, 107.7], 54 | [3.0, 2106.9, 74.8], 55 | [13.0, 1351.1, 56.9], 56 | [3.0, 2223.9, 82.5]] 57 | 58 | v3 = [[1.0, 4324.8, 165.4], 59 | [2.0, 2757.5, 116.1], 60 | [3.0, 2106.9, 74.8], 61 | [13.0, 1351.1, 56.9], 62 | [2.0, 2757.5, 116.1], 63 | [2.0, 2966.0, 107.7], 64 | [3.0, 2106.9, 74.8], 65 | [13.0, 1351.1, 56.9], 66 | [3.0, 2223.9, 82.5], 67 | [2.0, 2106.9, 74.8]] 68 | 69 | self.assertTrue(ec.all_non_dominated(v1)) 70 | self.assertFalse(ec.all_non_dominated(v2)) 71 | self.assertFalse(ec.all_non_dominated(v3)) 72 | 73 | self.assertTrue(ec.all_weakly_non_dominated(v1)) 74 | self.assertTrue(ec.all_weakly_non_dominated(v2)) 75 | self.assertFalse(ec.all_weakly_non_dominated(v3)) 76 | 77 | def test_run_econstraint_cplex_mip(self): 78 | from docplex.mp.model import Model as CplexModel 79 | 80 | config_logger(ec.logger, logging.DEBUG) 81 | 82 | # define model 83 | mdl = CplexModel() 84 | 85 | # define variables 86 | x = mdl.binary_var_dict(parks, name='x') 87 | y = mdl.binary_var_dict([(i, j) for i in buildings for j in parks], name='y') 88 | 89 | # define objective 90 | f1 = -mdl.sum(x[i] for i in parks) 91 | f2 = -mdl.sum(distance[i, j] * y[i, j] for i in buildings for j in parks) 92 | f3 = -mdl.max(distance[i, j] * y[i, j] for i in buildings for j in parks) 93 | objectives = [f1, f2, f3] 94 | 95 | # define constraints 96 | mdl.add_constraints(y[i, j] <= x[j] for i in buildings for j in parks) 97 | mdl.add_constraints(mdl.sum(y[i, j] for j in parks) >= 1 for i in buildings) 98 | 99 | pareto_frontier = [] 100 | 101 | def extract_solution(): 102 | xvals = {i: x[i].solution_value for i in parks} 103 | yvals = {(i, j): y[i, j].solution_value for i in buildings for j in parks} 104 | pareto_frontier.append((xvals, yvals)) 105 | 106 | # run e-constraint 107 | ec.run_econstraint(model=mdl, 108 | objectives=objectives, 109 | solution_extractor=extract_solution, 110 | optimizer='cplex') 111 | 112 | def f(i, sol): 113 | x, y = sol 114 | if i == 0: 115 | return sum(x[i] for i in parks) 116 | elif i == 1: 117 | return sum(distance[i, j] * y[i, j] for i in buildings for j in parks) 118 | else: 119 | return max(distance[i, j] * y[i, j] for i in buildings for j in parks) 120 | 121 | fvs = [[f(i, sol) for i in range(3)] for sol in pareto_frontier] 122 | 123 | for fv in fvs: 124 | a, b, c = fv 125 | print(f'[{a:>5.1f}, {b:>7.1f}, {c:>5.1f}],') 126 | 127 | self.assertTrue(ec.all_weakly_non_dominated(fvs)) 128 | 129 | def test_run_econstraint_cplex_lp(self): 130 | from docplex.mp.model import Model as CplexModel 131 | 132 | config_logger(ec.logger, logging.DEBUG) 133 | 134 | # define model 135 | mdl = CplexModel() 136 | 137 | # define variables 138 | x = mdl.continuous_var_dict(parks, name='x') 139 | y = mdl.continuous_var_dict([(i, j) for i in buildings for j in parks], name='y') 140 | 141 | # define objective 142 | f1 = -mdl.sum(x[i] for i in parks) 143 | f2 = -mdl.sum(distance[i, j] * y[i, j] for i in buildings for j in parks) 144 | f3 = -mdl.max(distance[i, j] * y[i, j] for i in buildings for j in parks) 145 | objectives = [f1, f2, f3] 146 | 147 | # define constraints 148 | mdl.add_constraints(y[i, j] <= x[j] for i in buildings for j in parks) 149 | mdl.add_constraints(mdl.sum(y[i, j] for j in parks) >= 1 for i in buildings) 150 | 151 | pareto_frontier = [] 152 | 153 | def extract_solution(): 154 | xvals = {i: x[i].solution_value for i in parks} 155 | yvals = {(i, j): y[i, j].solution_value for i in buildings for j in parks} 156 | pareto_frontier.append((xvals, yvals)) 157 | 158 | # run e-constraint 159 | ec.run_econstraint(model=mdl, 160 | objectives=objectives, 161 | solution_extractor=extract_solution, 162 | optimizer='cplex') 163 | 164 | def f(i, sol): 165 | x, y = sol 166 | if i == 0: 167 | return sum(x[i] for i in parks) 168 | elif i == 1: 169 | return sum(distance[i, j] * y[i, j] for i in buildings for j in parks) 170 | else: 171 | return max(distance[i, j] * y[i, j] for i in buildings for j in parks) 172 | 173 | fvs = [[f(i, sol) for i in range(3)] for sol in pareto_frontier] 174 | 175 | for fv in fvs: 176 | a, b, c = fv 177 | print(f'[{a:>5.1f}, {b:>7.1f}, {c:>5.1f}],') 178 | 179 | self.assertTrue(ec.all_non_dominated(fvs)) 180 | 181 | def test_run_econstraint_gurobi_mip(self): 182 | from gurobipy import Model as GurobiModel 183 | from gurobipy import GRB, quicksum 184 | 185 | # define model 186 | mdl = GurobiModel() 187 | 188 | # define variables 189 | x = mdl.addVars(parks, vtype=GRB.BINARY, name='x') 190 | y = mdl.addVars([(i, j) for i in buildings for j in parks], vtype=GRB.BINARY, name='y') 191 | z = mdl.addVars([0])[0] 192 | 193 | # define objective 194 | f1 = quicksum(-1 * x[i] for i in parks) 195 | f2 = quicksum(-1 * distance[i, j] * y[i, j] for i in buildings for j in parks) 196 | f3 = 0 - z 197 | objectives = [f1, f2, f3] 198 | 199 | # define constraints 200 | mdl.addConstrs(y[i, j] <= x[j] for i in buildings for j in parks) 201 | mdl.addConstrs(quicksum(y[i, j] for j in parks) >= 1 for i in buildings) 202 | mdl.addConstrs(distance[i, j] * y[i, j] <= z for i in buildings for j in parks) 203 | 204 | solutions = [] 205 | 206 | def extract_solution(): 207 | xvals = {i: x[i].x for i in parks} 208 | yvals = {(i, j): y[i, j].x for i in buildings for j in parks} 209 | zval = z.x 210 | solutions.append((xvals, yvals, zval)) 211 | 212 | ec.run_econstraint(model=mdl, 213 | objectives=objectives, 214 | solution_extractor=extract_solution, 215 | optimizer='gurobi') 216 | 217 | def f(i, sol): 218 | x, y, z = sol 219 | if i == 0: 220 | return sum(x[i] for i in parks) 221 | elif i == 1: 222 | return sum(distance[i, j] * y[i, j] for i in buildings for j in parks) 223 | else: 224 | return z 225 | 226 | fvs = [[f(i, sol) for i in range(3)] for sol in solutions] 227 | 228 | for fv in fvs: 229 | a, b, c = fv 230 | print(f'[{a:>5.1f}, {b:>7.1f}, {c:>5.1f}],') 231 | 232 | self.assertTrue(ec.all_weakly_non_dominated(fvs)) 233 | 234 | def test_run_econstraint_gurobi_lp(self): 235 | from gurobipy import Model as GurobiModel 236 | from gurobipy import GRB, quicksum 237 | 238 | # define model 239 | mdl = GurobiModel() 240 | 241 | # define variables 242 | x = mdl.addVars(parks, vtype=GRB.CONTINUOUS, name='x') 243 | y = mdl.addVars([(i, j) for i in buildings for j in parks], vtype=GRB.CONTINUOUS, name='y') 244 | z = mdl.addVars([0])[0] 245 | 246 | # define objective 247 | f1 = quicksum(-1 * x[i] for i in parks) 248 | f2 = quicksum(-1 * distance[i, j] * y[i, j] for i in buildings for j in parks) 249 | f3 = 0 - z 250 | objectives = [f1, f2, f3] 251 | 252 | # define constraints 253 | mdl.addConstrs(y[i, j] <= x[j] for i in buildings for j in parks) 254 | mdl.addConstrs(quicksum(y[i, j] for j in parks) >= 1 for i in buildings) 255 | mdl.addConstrs(distance[i, j] * y[i, j] <= z for i in buildings for j in parks) 256 | 257 | solutions = [] 258 | 259 | def extract_solution(): 260 | xvals = {i: x[i].x for i in parks} 261 | yvals = {(i, j): y[i, j].x for i in buildings for j in parks} 262 | zval = z.x 263 | solutions.append((xvals, yvals, zval)) 264 | 265 | ec.run_econstraint(model=mdl, 266 | 267 | objectives=objectives, 268 | solution_extractor=extract_solution, 269 | optimizer='gurobi') 270 | 271 | def f(i, sol): 272 | x, y, z = sol 273 | if i == 0: 274 | return sum(x[i] for i in parks) 275 | elif i == 1: 276 | return sum(distance[i, j] * y[i, j] for i in buildings for j in parks) 277 | else: 278 | return z 279 | 280 | fvs = [[f(i, sol) for i in range(3)] for sol in solutions] 281 | 282 | for fv in fvs: 283 | a, b, c = fv 284 | print(f'[{a:>5.1f}, {b:>7.1f}, {c:>5.1f}],') 285 | 286 | self.assertTrue(ec.all_non_dominated(fvs)) 287 | 288 | 289 | if __name__ == '__main__': 290 | unittest.main() 291 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------