├── data
├── README.md
└── code1


/data:
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1 | | Plant           | Max Capacity (MW) | Revenue per MW (\$) |
2 | | --------------- | ----------------: | ------------------: |
3 | | Solar\_Farm\_1  |                50 |                  70 |
4 | | Wind\_Plant\_1  |                60 |                  90 |
5 | | Hydro\_Plant\_1 |                40 |                  60 |
6 | | Solar\_Farm\_2  |                30 |                  65 |
7 | | Wind\_Plant\_2  |                20 |                  80 |
8 | 


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/README.md:
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 1 | # ⚡ Resource Allocation Optimization
 2 | 
 3 | This project demonstrates how to solve a **resource allocation optimization problem** using Python and linear programming (via the `PuLP` library). The scenario focuses on allocating a limited amount of power (in megawatts) across various renewable energy plants to **maximize total revenue**.
 4 | 
 5 | ## 📈 Problem Statement
 6 | 
 7 | We are given a set of renewable energy plants with:
 8 | - Maximum power generation capacities
 9 | - Revenue earned per MW of power produced
10 | 
11 | Given a fixed amount of total power (100 MW), we want to allocate power to these plants such that:
12 | 1. No plant exceeds its generation capacity.
13 | 2. Total power used does not exceed the available 100 MW.
14 | 3. Revenue is maximized.
15 | 
16 | ## 🔢 Data
17 | 
18 | | Plant         | Max Capacity (MW) | Revenue per MW ($) |
19 | |---------------|------------------:|--------------------:|
20 | | Solar_Farm_1  | 50               | 70                 |
21 | | Wind_Plant_1  | 60               | 90                 |
22 | | Hydro_Plant_1 | 40               | 60                 |
23 | | Solar_Farm_2  | 30               | 65                 |
24 | | Wind_Plant_2  | 20               | 80                 |
25 | 
26 | - **Total Available Power**: 100 MW
27 | 
28 | ## 🧠 Objective Function
29 | 
30 | Maximize:
31 | 
32 | 


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/code1:
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 1 | # Install PuLP if not already installed:
 2 | # pip install pulp
 3 | 
 4 | from pulp import LpMaximize, LpProblem, LpVariable, lpSum
 5 | 
 6 | # Example: Allocate resources to 3 projects with resource constraints
 7 | 
 8 | # Projects
 9 | projects = ["Project_A", "Project_B", "Project_C"]
10 | 
11 | # Profit per unit resource allocated to each project
12 | profit = {"Project_A": 30, "Project_B": 20, "Project_C": 40}
13 | 
14 | # Max resource allowed per project
15 | max_resource = {"Project_A": 30, "Project_B": 40, "Project_C": 20}
16 | 
17 | # Total available resources
18 | total_resources = 60
19 | 
20 | # Define the LP problem
21 | model = LpProblem("Resource-Allocation", LpMaximize)
22 | 
23 | # Define variables
24 | resource_alloc = {p: LpVariable(f"alloc_{p}", lowBound=0, upBound=max_resource[p]) for p in projects}
25 | 
26 | # Objective: Maximize total profit
27 | model += lpSum(profit[p] * resource_alloc[p] for p in projects), "Total_Profit"
28 | 
29 | # Constraint: Do not exceed total available resources
30 | model += lpSum(resource_alloc[p] for p in projects) <= total_resources, "Resource_Limit"
31 | 
32 | # Solve the model
33 | model.solve()
34 | 
35 | # Output results
36 | print("Status:", model.status)
37 | for p in projects:
38 |     print(f"Allocate to {p}: {resource_alloc[p].varValue} units")
39 | print("Total Profit:", model.objective.value())
40 | 


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