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draft code generation.py
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draft code generation.py
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# reference help
# https://www.supplychaindataanalytics.com/multi-objective-linear-optimization-with-pulp-in-python/#:~:text=A%20multi%2Dobjective%20linear%20optimization%20problem%20is%20a%20linear%20optimization,or%20multi%2Dgoal%20linear%20programming.
# https://web.stanford.edu/group/sisl/k12/optimization/MO-unit5-pdfs/5.8Pareto.pdf
# this is rough idea of a problem
# please recheck your own problem
import pandas as pand
import numpy as np
import gurobipy as grb
import matplotlib.pyplot as plt
from pulp import *
# variables
warehouse_limits = "warehouse_limits"
customer_demands = "customer_demands"
fixed_costs = "fixed_costs"
cost_matrix = "cost_matrix"
demand = np.loadtxt("./probability_multiply_demand.csv", delimiter=",", dtype=int)
print('demand', demand)
def get_test_cases():
a = np.loadtxt("./capacity.csv", delimiter=",", dtype=int)
pd = demand
f = np.loadtxt("./fixed_cost.csv", delimiter=",", dtype=int)
c = np.loadtxt("./cost_matrix.csv", delimiter=",", dtype=float)
s = np.loadtxt("./storage_cost.csv", delimiter=",", dtype=float)
return a, pd, f, c, s
"""""
# For Gurobi Solver
"""""
SetObjWeight = []
w = 0
for i in range(101):
weightage = [w, 1 - w]
SetObjWeight.append(weightage)
w += 0.01
print('weightage combination ', SetObjWeight)
def evaluate_fitness():
a, pd, f, c, s = get_test_cases()
n = len(a) # warehouse
m = len(pd) # customer
warehouses = range(n)
customers = range(m)
# model
model = grb.Model("multi obj")
# decision variables
demand_transfer = model.addVars(warehouses, customers, vtype=grb.GRB.INTEGER, name="demand_transfer")
open_or_close = model.addVars(warehouses, vtype=grb.GRB.BINARY, name="open_or_close")
# demand constraints
model.addConstrs((demand_transfer.sum('*', j) == pd[j] for j in customers), "Demand")
# capacity constraints
model.addConstrs((demand_transfer.sum(i) <= a[i] * open_or_close[i] for i in warehouses), "Capacity")
# domino constraints
model.addConstrs(open_or_close[i] == 0 for i in warehouses if i == 4)
model.addConstrs(demand_transfer[i, j] <= pd[j] * open_or_close[i] for i in warehouses for j in customers)
# this is minimization
model.ModelSense = grb.GRB.MINIMIZE
# Limit how many solutions to collect
model.setParam(grb.GRB.Param.PoolSolutions, 100)
# multi-objective
# Set and configure p-th objective
objective_storing = []
for wei in range(len(SetObjWeight)):
decision_variable = []
for p in range(2):
if p == 0:
objective = SetObjWeight[wei][p]*(sum([f[i] * open_or_close[i] for i in warehouses]) + \
(grb.quicksum(c[i, j] * demand_transfer[i, j] for i in warehouses for j in customers)))
elif p == 1:
objective = 2*SetObjWeight[wei][p]*(sum(a[i] * open_or_close[i] for i in warehouses) - \
(grb.quicksum(demand_transfer[i, j] for i in warehouses for j in customers)))
model.setObjectiveN(objective, index = p)
# Optimize
model.optimize()
objective_storing.append(model.ObjNVal)
print(f"Obj = {model.ObjNVal}")
# Save problem
model.write('multi obj.lp')
nSolutions = model.SolCount
nObjectives = model.NumObj
nVariables = model.numVars
print('Problem has', nObjectives, 'objectives for weightage ', SetObjWeight[wei] )
print('Gurobi found', nSolutions, 'solutions for weightage ', SetObjWeight[wei] )
print('found variables', nVariables, 'variables for weightage ', SetObjWeight[wei] )
for var in model.getVars():
if var.varName:
decision_variable.append(abs(var.X))
print_decision = np.array(decision_variable)
print("decision variable array for weightage \n", SetObjWeight[wei], print_decision.reshape(10, 9))
# last row is binary variable
# Status checking
status = model.Status
if status in (grb.GRB.INF_OR_UNBD, grb.GRB.INFEASIBLE, grb.GRB.UNBOUNDED):
print("The model cannot be solved because it is infeasible or "
"unbounded")
sys.exit(1)
if status != grb.GRB.OPTIMAL:
print('Optimization was stopped with status ' + str(status))
sys.exit(1)
print_objective_all = np.array(objective_storing)
print("all objective ", print_objective_all)
# print("all objective ", print_objective_all.reshape(len(SetObjWeight), 1))
abc = np.array(SetObjWeight)
bcd = np.array(print_objective_all)
xy = abc * bcd[:, np.newaxis]
x = [i[0] for i in xy]
y = [i[1] for i in xy]
print(y)
plt.figure(figsize=(5,5), dpi=160)
plt.title("")
plt.xlabel("F1")
plt.ylabel("F2")
plt.scatter(x, y, color="black", s=4.5)
plt.axvline(x=305, linestyle='--', linewidth=0.5, color='black')
plt.axhline(y=700, linestyle='--', linewidth=0.5, color='black')
xx, yy = [min(x), max(x)], [max(y), min(y)]
plt.plot(xx, yy, color='black')
plt.rcParams.update({'font.size': 11})
plt.show()
return
"""""
#For PuLP Solver
"""""
a, pd, f, c, s = get_test_cases()
n = len(a) # warehouse
m = len(pd) # customer
warehouses = range(n)
customers = range(m)
facility = [0, 1, 2, 3, 4, 5, 6, 7, 8]
demand_area = [0, 1, 2, 3, 4, 5, 6, 7, 8]
nm = [(i, j) for i in warehouses for j in customers]
# define step-size
stepSize = 0.01
# initialize empty DataFrame for storing optimization outcomes
solutionTable = pand.DataFrame(columns=["weight", "obj_value"])
# iterate through alpha values from 0 to 1 with stepSize, and write PuLP solutions into solutionTable
pulp_objective = []
for w in range(0, 101, int(stepSize * 100)):
# declare the problem again
linearProblem = LpProblem("Multi-objective linear minimization", LpMinimize)
# declare optimization variables, using PuLP
open_or_close = LpVariable.dicts("open_or_close", facility, 0, 1, cat=LpBinary)
demand_transfer = LpVariable.dicts("demand_transfer", nm, lowBound=0, cat=LpInteger)
# add the objective function at sampled alpha
linearProblem += ((w / 100) * (lpSum(f[i] * open_or_close[i] for i in facility) +
(lpSum(c[i, j] * demand_transfer[i, j] for i in facility for j in demand_area)
))) + (2 * (1 - w / 100) * (lpSum(a[i] * open_or_close[i] for i in facility) -
(lpSum(demand_transfer[i, j] for i in facility
for j in demand_area))))
# add the constraints
for j in demand_area: # demand constraint
linearProblem += lpSum(demand_transfer[(i, j)] for i in facility) == pd[j]
for i in facility: # capacity constraint
linearProblem += lpSum(demand_transfer[(i, j)] for j in demand_area) <= a[i] * open_or_close[i]
for i in facility: # domino location not select constraint
if i == 4:
linearProblem += open_or_close[i] == 0
else:
continue
for j in demand_area:
for i in facility:
linearProblem += demand_transfer[(i, j)] <= pd[j] * open_or_close[i]
# solve the problem
linearProblem.solve()
print("Status:", LpStatus[linearProblem.status])
for v in linearProblem.variables():
print(w, v.name, "=", v.varValue)
pulp_objective.append(value(linearProblem.objective))
# write solutions into DataFrame
solutionTable.loc[int(w / (stepSize * 100))] = [w / 100,
value(linearProblem.objective)]
file_name = 'solution_data.csv'
for_csv_soln = solutionTable
for_csv_soln.to_csv(file_name)
print(solutionTable)
print("pulp obj \n ", pulp_objective)
dc = np.array(SetObjWeight)
gh = np.array(pulp_objective)
xyz=dc * gh[:, np.newaxis]
xa=[i[0] for i in xyz]
yb=[i[1] for i in xyz]
print(xyz)
plt.figure(figsize=(5, 5),dpi=170)
plt.title("")
plt.xlabel("F1")
plt.ylabel("F2")
plt.scatter(xa, yb, color ="black",s = 4)
plt.rcParams.update({'font.size': 11})
#ax = plt.gca()
#ax.set_ylim(ax.get_ylim()[::-1])
plt.axvline(x=305, linestyle = '--', linewidth = 0.5, color='black')
plt.axhline(y=700, linestyle = '--', linewidth = 0.5, color='black')
xxy, yyx = [min(xa), max(xa)], [max(yb), min(yb)]
plt.plot(xxy, yyx, color='black')
plt.show()
def main():
evaluate_fitness()
if __name__ == "__main__":
main()