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8bb3480 6ee2f87 8bb3480 6ee2f87 8bb3480 6ee2f87 8bb3480 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | from pulp import *
import pandas as pd
from read_data import load_excel_data
from create_optimization_problem import create_sourcing_problem
def solve_sourcing_problem(prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable):
"""
Solves the sourcing optimization problem using PuLP.
Parameters:
prob (LpProblem): The linear programming problem instance.
Warehouses (list): List of warehouse names.
Products (list): List of product names.
Stock (dict): Dictionary containing initial stock levels for each warehouse and product.
Priority (dict): Dictionary containing priority levels for each order.
Orders (list): List of order identifiers.
Quantity (dict): Dictionary of order quantities for each product.
Variable (dict): Dictionary of PuLP variables representing the decision variables.
Returns:
tuple: A tuple containing a string and two DataFrames:
- LpStatus[prob.status] (str): The status of the solution.
- fulfillment_solution: Contains details of supply quantities for each order from each warehouse.
- warehouse_stock_status: Contains initial stock, supplied stock, and remaining stock levels.
"""
# The problem is solved using PuLP's choice of Solver
prob.solve()
# The status of the solution is printed to the screen
print("***** Solution Status *****")
print("Status:", LpStatus[prob.status])
# Fulfillment Solution
fulfillment_solution = []
for w in Warehouses:
for p in Products:
for o in Orders:
fulfillment_solution.append({
"Warehouse": w,
"Product": p,
"Order": o,
"Supply Quantity": Variable[w][o][p].varValue
})
fulfillment_solution = pd.DataFrame(fulfillment_solution)
# Stock Status
warehouse_stock_status = []
for w in Warehouses:
for p in Products:
warehouse_stock_status.append({
"Warehouse": w,
"Product": p,
"Initial Stock": Stock[w][p],
"Supplied Stock": sum(Variable[w][o][p].varValue for o in Orders),
"Remaining Stock": Stock[w][p] - sum(Variable[w][o][p].varValue for o in Orders)
})
warehouse_stock_status = pd.DataFrame(warehouse_stock_status)
return LpStatus[prob.status], fulfillment_solution, warehouse_stock_status
if __name__ == "__main__":
filepath = 'Intelligent_Sourcing.xlsx' # Example file path
weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df, = load_excel_data(filepath)
# Create LP Problem
prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable = create_sourcing_problem(weightage_dict, priority_df, warehouse_df, order_df, cost_df, distance_df, days_df)
#print(prob.objective)
# Solve LP Problem
status, fulfillment_solution, warehouse_stock_status = solve_sourcing_problem(prob, Warehouses, Products, Stock, Priority, Orders, Quantity, Variable)
print(fulfillment_solution)
print(warehouse_stock_status)
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