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main.py
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main.py
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import functools
import timeit
import numpy as np
import matplotlib.pyplot as plt
from os import listdir
from evrptw_meta import VariableNeighbourhoodSearch, SimulatedAnnealing
from evrptw_solver import EVRPTWSolver
from evrptw_utilities import load_problem_instance, load_solution, write_solution_to_file, write_solution_stats_to_file, \
write_meta_heuristic_result_statistic_to_file
from heuristics.construction.beasley_heuristic import BeasleyHeuristic, k_nearest_neighbor_min_due_date, \
k_nearest_neighbor_min_ready_time, nearest_neighbor_tolerance_min_due_date, \
nearest_neighbor_tolerance_min_ready_time
RESULT_STATISTICS_FILENAME = 'ex1_result_1126205.csv'
RESULT_STATISTICS_LATEX_TABLE = 'ex1_result_1126205.tex'
MAX_ITERATIONS = 10
def main():
# best_score, best_heuristic, best_param = find_best_heuristic_setting_experiment()
# CACHING OF BEST RESULTS
best_score = 0
best_heuristic = nearest_neighbor_tolerance_min_ready_time
best_param = 1.8
print("The best score of {0} was achieved with {1} and parameter {2}".format(best_score, best_heuristic,
round(best_param, 2)))
print()
print("============================================")
print("Generate initial solutions...")
print("============================================")
print()
construction_heuristic = BeasleyHeuristic(best_heuristic, [best_param])
test_case_statistics = []
solver = EVRPTWSolver(construction_heuristic)
for file in listdir('_problem_instances/exercise_instances/'):
if file.endswith('.txt'):
print('process file {0}'.format(file))
print('load problem instance...')
problem_instance = load_problem_instance('_problem_instances/exercise_instances/' + file)
print('generate routes...')
duration = timeit.timeit(functools.partial(solver.solve, problem_instance), number=1) * 1000
distance, solution = solver.solve(problem_instance)
test_case_statistics.append((file, distance, duration))
print('write results to file ...')
write_solution_to_file("_problem_solutions/solution_{0}".format(file), distance, solution)
print()
test_case_statistics.sort(key=lambda x: x[0])
write_solution_stats_to_file(RESULT_STATISTICS_FILENAME, test_case_statistics)
write_solution_stats_to_file(RESULT_STATISTICS_LATEX_TABLE, test_case_statistics, style='latex')
print()
print("============================================")
print("Apply meta-heuristic to improve solutions...")
print("============================================")
print()
dist_statistic = dict()
time_statistic = dict()
for file in listdir('_problem_instances/exercise_instances/'):
if file.endswith('.txt'):
print("process file {0}".format(file))
problem_instance = load_problem_instance('_problem_instances/exercise_instances/' + file)
distance, solution = load_solution('_problem_solutions/solution_{0}'.format(file))
print('start to improve the routes...')
dist_statistic[file] = []
time_statistic[file] = []
for i in range(0, MAX_ITERATIONS):
meta_heuristic = SimulatedAnnealing(problem_instance, solution, distance, 0.5, 0.8, '{0}_{1}'.format(file,i))
new_distance, new_solution = meta_heuristic.improve_solution()
duration = timeit.timeit(meta_heuristic.improve_solution, number=1)
time_statistic[file].append(duration)
dist_statistic[file].append(new_distance)
print('solution improved by {0}'.format(distance - new_distance))
print("write results to file...")
write_solution_to_file("_meta_solutions/solution_{0}_{1}".format(i, file), new_distance, new_solution)
print()
write_meta_heuristic_result_statistic_to_file('meta_heuristic_results.csv',dist_statistic,time_statistic)
print("done")
def find_best_heuristic_setting_experiment():
print('NN tolerance heuristic with min due date:')
print('================================')
print('tolerance; average score')
best_param = -1
best_heuristic = None
best_score = 0
nnt_deadline_results = []
nnt_readytime_results = []
knn_deadline_results = []
knn_readytime_results = []
for tolerance in np.arange(1, 3, 0.1):
distances = []
construction_heuristic = BeasleyHeuristic(nearest_neighbor_tolerance_min_due_date, [round(tolerance, 2)])
test_case_statistics = []
solver = EVRPTWSolver(construction_heuristic)
for file in listdir('_problem_instances/exercise_instances/'):
if file.endswith('.txt'):
problem_instance = load_problem_instance('_problem_instances/exercise_instances/' + file)
distance, solution = solver.solve(problem_instance)
distances.append(distance)
if best_score == 0 or best_score > np.mean(distances):
best_score = np.mean(distances)
best_heuristic = nearest_neighbor_tolerance_min_due_date
best_param = round(tolerance, 2)
nnt_deadline_results.append(np.mean(distances))
print("{0:.2f}; {1:.2f}".format(tolerance, np.mean(distances)))
print('NN tolerance heuristic with min ready time:')
print('================================')
print('tolerance; average score')
for tolerance in np.arange(1, 3, 0.1):
distances = []
construction_heuristic = BeasleyHeuristic(nearest_neighbor_tolerance_min_ready_time, [round(tolerance, 2)])
solver = EVRPTWSolver(construction_heuristic)
for file in listdir('_problem_instances/exercise_instances/'):
if file.endswith('.txt'):
problem_instance = load_problem_instance('_problem_instances/exercise_instances/' + file)
distance, solution = solver.solve(problem_instance)
write_solution_to_file("_problem_solutions/solution_{0}".format(file), distance, solution)
distances.append(distance)
if best_score == 0 or best_score > np.mean(distances):
best_score = np.mean(distances)
best_heuristic = nearest_neighbor_tolerance_min_ready_time
best_param = round(tolerance, 2)
nnt_readytime_results.append(np.mean(distances))
print("{0:.2f}; {1:.2f}".format(tolerance, np.mean(distances)))
plt.title('NN heuristic with tolerance')
deadline, = plt.plot(np.arange(1, 3, 0.1), nnt_deadline_results, label='deadline minimized')
readytime, = plt.plot(np.arange(1, 3, 0.1), nnt_readytime_results, label='readytime minimized')
plt.xlabel('tolerance')
plt.ylabel('average score')
plt.legend([deadline, readytime], ['deadline minimized', 'readytime minimized'])
plt.show()
print('kNN heuristic with min due date:')
print('================================')
print('k; average score')
for k in range(1, 10):
distances = []
construction_heuristic = BeasleyHeuristic(k_nearest_neighbor_min_due_date, [round(k, 2)])
solver = EVRPTWSolver(construction_heuristic)
for file in listdir('_problem_instances/exercise_instances/'):
if file.endswith('.txt'):
problem_instance = load_problem_instance('_problem_instances/exercise_instances/' + file)
distance, solution = solver.solve(problem_instance)
write_solution_to_file("_problem_solutions/solution_{0}".format(file), distance, solution)
distances.append(distance)
if best_score == 0 or best_score > np.mean(distances):
best_score = np.mean(distances)
best_heuristic = k_nearest_neighbor_min_due_date
best_param = k
knn_deadline_results.append(np.mean(distances))
print("{0:.2f}; {1:.2f}".format(k, np.mean(distances)))
print('kNN heuristic with min ready time:')
print('================================')
print('k; average score')
for k in range(1, 10):
distances = []
construction_heuristic = BeasleyHeuristic(k_nearest_neighbor_min_ready_time, [round(k, 2)])
solver = EVRPTWSolver(construction_heuristic)
for file in listdir('_problem_instances/exercise_instances/'):
if file.endswith('.txt'):
problem_instance = load_problem_instance('_problem_instances/exercise_instances/' + file)
distance, solution = solver.solve(problem_instance)
write_solution_to_file("_problem_solutions/solution_{0}".format(file), distance, solution)
distances.append(distance)
if best_score == 0 or best_score > np.mean(distances):
best_score = np.mean(distances)
best_heuristic = k_nearest_neighbor_min_ready_time
best_param = k
knn_readytime_results.append(np.mean(distances))
print("{0:.2f}; {1:.2f}".format(k, np.mean(distances)))
plt.title('kNN heuristic')
deadline, = plt.plot(range(1, 10), knn_deadline_results, label='deadline minimized')
readytime, = plt.plot(range(1, 10), knn_readytime_results, label='readytime minimized')
plt.xlabel('k')
plt.ylabel('average score')
plt.legend([deadline, readytime], ['deadline minimized', 'readytime minimized'])
plt.show()
return best_score, best_heuristic, best_param
if __name__ == "__main__":
main()