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main.py
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main.py
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import utils
from Network import Network
from Node import Node
from Formulation import Formulation
import pandas as pd
from docplex.mp.solution import SolveSolution
import os.path
import pickle
"""
n: station number
N: stations without deposit
V: stations with deposit
A: arcs
m: vehicle number
Q: vehicle capacity
q: demand at stations
c: cost matrix
"""
df = pd.DataFrame(columns=['Instance', 'Our obj', 'Paper Obj', 'Our Time', 'Paper Time', 'GAP'])
datasets = [
{"instance": "1Bari30.txt", "obj": 14600, "time": "0.06"},
{"instance": "2Bari20.txt", "obj": 15700, "time": "0.06"},
{"instance": "3Bari10.txt", "obj": 20600, "time": "0.16"},
{"instance": "4ReggioEmilia30.txt", "obj": 16900, "time": "0.03"},
{"instance": "5ReggioEmilia20.txt", "obj": 23200, "time": "0.09"},
{"instance": "6ReggioEmilia10.txt", "obj": 32500, "time": "5.59"},
{"instance": "7Bergamo30.txt", "obj": 12600, "time": "0.05"},
{"instance": "8Bergamo20.txt", "obj": 12700, "time": "0.06"},
{"instance": "9Bergamo12.txt", "obj": 13500, "time": "0.27"},
{"instance": "10Parma30.txt", "obj": 29000, "time": "0.05"},
{"instance": "11Parma20.txt", "obj": 29000, "time": "0.05"},
{"instance": "12Parma10.txt", "obj": 32500, "time": "0.22"},
{"instance": "13Treviso30.txt", "obj": 29259, "time": "0.12"},
{"instance": "14Treviso20.txt", "obj": 29259, "time": "0.12"},
{"instance": "15Treviso10.txt", "obj": 31443, "time": "0.27"},
{"instance": "16LaSpezia30.txt", "obj": 20746, "time": "0.09"},
{"instance": "17LaSpezia20.txt", "obj": 20746, "time": "0.09"},
{"instance": "18LaSpezia10.txt", "obj": 22811, "time": "0.16"},
{"instance": "19BuenosAires30.txt", "obj": 76999 , "time": "1.36"},
{"instance": "20BuenosAires20.txt", "obj": 91619, "time": "23.26"},
{"instance": "21Ottawa30.txt", "obj": 16202, "time": "0.06"},
{"instance": "22Ottawa20.txt", "obj": 16202, "time": "0.06"},
{"instance": "23Ottawa10.txt", "obj": 17576, "time": "0.3"},
{"instance": "24SanAntonio30.txt", "obj": 22982 , "time": "0.19"},
{"instance": "25SanAntonio20.txt", "obj": 24007, "time": "3.63"},
{"instance": "27Brescia30.txt", "obj": 30300, "time": "0.7"},
{"instance": "28Brescia20.txt", "obj": 31100, "time": "6.07"},
{"instance": "29Brescia11.txt", "obj": 35200, "time": "24.46"},
{"instance": "30Roma30.txt", "obj": 61900, "time": "4.27"},
{"instance": "31Roma20.txt", "obj": 66600, "time": "22.04"},
{"instance": "32Roma18.txt", "obj": 68300, "time": "16.15"},
{"instance": "33Madison30.txt", "obj": 29246, "time": "0.09"},
{"instance": "34Madison20.txt", "obj": 29839, "time": "0.31"},
{"instance": "35Madison10.txt", "obj": 33848, "time": "6.02"},
{"instance": "36Guadalajara30.txt", "obj": 57476, "time": "1.16"},
{"instance": "37Guadalajara20.txt", "obj": 59493, "time": "2.29"}
]
datasets = [ {"instance": "1Bari30.txt", "obj": 14600, "time": "0.06"},
{"instance": "2Bari20.txt", "obj": 15700, "time": "0.06"},
{"instance": "3Bari10.txt", "obj": 20600, "time": "0.16"},
{"instance": "4ReggioEmilia30.txt", "obj": 16900, "time": "0.03"},
{"instance": "5ReggioEmilia20.txt", "obj": 23200, "time": "0.09"},
{"instance": "6ReggioEmilia10.txt", "obj": 32500, "time": "5.59"},
{"instance": "7Bergamo30.txt", "obj": 12600, "time": "0.05"},
{"instance": "8Bergamo20.txt", "obj": 12700, "time": "0.06"},
{"instance": "9Bergamo12.txt", "obj": 13500, "time": "0.27"},
{"instance": "10Parma30.txt", "obj": 29000, "time": "0.05"},
{"instance": "11Parma20.txt", "obj": 29000, "time": "0.05"},
{"instance": "12Parma10.txt", "obj": 32500, "time": "0.22"},
{"instance": "13Treviso30.txt", "obj": 29259, "time": "0.12"},
{"instance": "14Treviso20.txt", "obj": 29259, "time": "0.12"},
{"instance": "15Treviso10.txt", "obj": 31443, "time": "0.27"},
{"instance": "16LaSpezia30.txt", "obj": 20746, "time": "0.09"},
{"instance": "17LaSpezia20.txt", "obj": 20746, "time": "0.09"},
{"instance": "18LaSpezia10.txt", "obj": 22811, "time": "0.16"}]
for dataset in datasets:
print(dataset["instance"])
# read dataset
n, c, q, Q = utils.open_dataset("dataset/" + dataset["instance"])
N = [i for i in range(1, n)]
V = [0] + N
A = [(i, j) for i in V for j in V]
#m = 80
if os.path.isfile("dataset/solutions/" + dataset["instance"] + ".sol"):
# check if a solution file exists
with open("dataset/solutions/" + dataset["instance"] + ".sol", 'rb') as input:
sol_object = pickle.load(input)
total_cost = sol_object["cost"]
value_map = sol_object["value_map"]
m = 12
else:
# build initial solution
source = Node(0, q[0])
nodes = [Node(i, q[i]) for i in range(1, n)]
network = Network(source, c, Q)
network.add_nodes(nodes)
routes, total_cost = network.build_route()
print([node.id for route in routes for node in route])
# the route found by the heuristic should have less or equal number of vehicle
#assert len(routes[0]) <= m
m = len(routes)
# save initial solution as CPLEX file
value_map = utils.write_cplex_solution(routes, n)
# cplex solution
f = Formulation(A, V, N, q, Q, c, m, n, 3)
f.set_formulation()
f.add_formulation_constraints()
solve_solution = SolveSolution(model=f.formulation, var_value_map=value_map, obj=total_cost)
solution, routes_solution = f.run_formulation(solve_solution, False)
utils.print_routes(routes_solution)
if solution is None:
new_value = {'Instance': dataset["instance"], 'Our obj': "None", 'Paper Obj': dataset["obj"], 'Our Time': "none", 'Paper Time': dataset["time"], 'GAP': "None"}
else:
solve_details = solution.solve_details
new_value = {'Instance': dataset["instance"], 'Our obj': solution.get_objective_value(), 'Paper Obj': dataset["obj"], 'Our Time': float("{:.2f}".format(solve_details.time)), 'Paper Time': dataset["time"], 'GAP': float("{:.2f}".format(100*solution.get_objective_value()/dataset["obj"]-100))}
print(new_value)
df = df.append(new_value, ignore_index=True)
print(df)
print("Time avg:", df["Our Time"].mean())
print("GAP avg:", df["GAP"].mean())
df.to_csv('df.csv')