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mip-refine-nearest.py
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#!/usr/bin/python
# -*- coding: utf-8 -*-
import math
import os
import numpy as np
import time
from collections import namedtuple
from ortools.linear_solver import pywraplp
Point = namedtuple("Point", ['x', 'y'])
Facility = namedtuple("Facility", ['index', 'setup_cost', 'capacity', 'location'])
Customer = namedtuple("Customer", ['index', 'demand', 'location'])
def length(point1, point2):
return math.sqrt((point1.x - point2.x)**2 + (point1.y - point2.y)**2)
def solve_it(input_data):
lines = input_data.split('\n')
parts = lines[0].split()
facility_count = int(parts[0])
customer_count = int(parts[1])
facilities = []
for i in range(1, facility_count + 1):
parts = lines[i].split()
facilities.append(Facility(i - 1, float(parts[0]), int(parts[1]), Point(float(parts[2]), float(parts[3])) ))
customers = []
for i in range(facility_count + 1, facility_count + 1 + customer_count):
parts = lines[i].split()
customers.append(Customer(i - 1 - facility_count, int(parts[0]), Point(float(parts[1]), float(parts[2]))))
# distance_matrix[i][j] is the distance between the i-th customer and the j-th facility.
distance_matrix = [[((customer.location.x - facility.location.x) ** 2 + (customer.location.y - facility.location.y) ** 2) ** 0.5 \
for facility in facilities] for customer in customers]
# distance_matrix_facilities[i][j] is the distance between the i-th facility and the j-th facility.
distance_matrix_facilities = [[((fa.location.x - fb.location.x) ** 2 + (fa.location.y - fb.location.y) ** 2) ** 0.5 \
for fb in facilities] for fa in facilities]
# distance_matrix_facilities_indice[i] contains the indice of the nearest facilities for the i-th facility.
distance_matrix_facilities_indice = [[i for i in range(len(facilities))] for j in range(len(facilities))]
for i, row in enumerate(distance_matrix_facilities_indice):
row.sort(key=lambda x: distance_matrix_facilities[i][x])
# read initial solution generated by Guided Local Search.
with open("cpp_output.txt", 'r') as assignment_init_file:
assignment_init = assignment_init_file.read()
assignment_init = assignment_init.split('\n')
objective_init = float(assignment_init[0].split()[0])
assignment_init = assignment_init[1].split()
assignment_init = [int(index_str) for index_str in assignment_init]
best_objective = objective_init
best_assignment = assignment_init
best_facility_open = [0] * len(facilities)
for index in best_assignment:
best_facility_open[index] = 1
best_output = None
# number of facilities to form a sub-problem.
n_sub_facilities = 50
round_limit = 10000000
for round in range(round_limit):
start_time = time.time()
# Randomly choose a facility and its top-n nearest neighbor facilities.
sub_facilities = np.random.choice(len(facilities))
sub_facilities = distance_matrix_facilities_indice[sub_facilities][:n_sub_facilities]
sub_facilities_set = set(sub_facilities)
# Select all customers that are served by the above facilities.
sub_customers = [i for i in range(len(customers)) if best_assignment[i] in sub_facilities_set]
objective_old = 0.0
for customer in sub_customers:
objective_old += distance_matrix[customer][best_assignment[customer]]
for facility in sub_facilities:
objective_old += best_facility_open[facility] * facilities[facility].setup_cost
solver = pywraplp.Solver('SolveIntegerProblem', pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)
sub_assignment = [[solver.IntVar(0.0, 1.0, 'a' + str(i) + ',' + str(j)) for j in range(len(sub_facilities))] for i in range(len(sub_customers))]
sub_facility_open = [solver.IntVar(0.0, 1.0, 'f' + str(j)) for j in range(len(sub_facilities))]
# Constraint: each customer must be assigned to exactly one facility.
for i in range(len(sub_customers)):
solver.Add(sum([sub_assignment[i][j] for j in range(len(sub_facilities))]) == 1)
# Constraint: a customer must be assigned to an open facility.
for i in range(len(sub_customers)):
for j in range(len(sub_facilities)):
solver.Add(sub_assignment[i][j] <= sub_facility_open[j])
# Constraint: the capacity of each facility must not be exceeded.
for j in range(len(sub_facilities)):
solver.Add(sum([sub_assignment[i][j] * customers[sub_customers[i]].demand \
for i in range(len(sub_customers))]) <= facilities[sub_facilities[j]].capacity)
objective = solver.Objective()
# Objective: sum all the distance.
for i in range(len(sub_customers)):
for j in range(len(sub_facilities)):
objective.SetCoefficient(sub_assignment[i][j], distance_matrix[sub_customers[i]][sub_facilities[j]])
# Objective: sum all the setup cost.
for j in range(len(sub_facilities)):
objective.SetCoefficient(sub_facility_open[j], facilities[sub_facilities[j]].setup_cost)
objective.SetMinimization()
"""Solve the problem and print the solution."""
SEC = 1000
MIN = 60 * SEC
solver.SetTimeLimit(1 * MIN)
result_status = solver.Solve()
end_time = time.time()
if result_status != solver.OPTIMAL and result_status != solver.FEASIBLE:
print('[Round %9d/%9d] [N-Sub-Facilities %4d] [Best Objective %f] [Old Objective %f] [New Objective N/A] [Time %f]' % \
(round + 1, round_limit, n_sub_facilities, best_objective, objective_old, end_time - start_time))
continue
objective_new = solver.Objective().Value()
assignment_new = []
for i in range(len(sub_customers)):
for j in range(len(sub_facilities)):
if sub_assignment[i][j].solution_value() == 1:
assignment_new.append(sub_facilities[j])
break
print('[Round %9d/%9d] [N-Sub-Facilities %4d] [Best Objective %f] [Old Objective %f] [New Objective %f] [Time %f] %s' % \
(round + 1, round_limit, n_sub_facilities, best_objective, objective_old, objective_new, end_time - start_time, \
'best model found' if objective_old >= objective_new + 1 else ''))
if objective_old >= objective_new + 1:
best_objective -= objective_old - objective_new
for i, j in enumerate(assignment_new):
best_assignment[sub_customers[i]] = j
best_facility_open = [0] * len(facilities)
for index in best_assignment:
best_facility_open[index] = 1
best_output = str(best_objective) + ' ' + '0' + '\n' + ' '.join([str(assign) for assign in best_assignment])
with open("mip-output.txt", 'w') as best_mip_output_file:
best_mip_output_file.write(best_output)
return best_output
import sys
if __name__ == '__main__':
import sys
if len(sys.argv) > 1:
file_location = sys.argv[1].strip()
with open(file_location, 'r') as input_data_file:
input_data = input_data_file.read()
print(solve_it(input_data))
else:
print('This test requires an input file. Please select one from the data directory. (i.e. python solver.py ./data/fl_16_2)')