def solve(self, data, vehicles, timeout):
"""Solves the CVRP problem using Monte Carlo Savings method (OLIVEIRA, 2014)
Parameters:
data: CVRPData instance
vehicles: Vehicles number
timeout: max processing time in seconds
Returns a solution (MonteCarloSavingsSolution class))
"""
start = time.time()
time_found = None
best = MonteCarloSavingsSolution(data, vehicles)
best_feasible = best
savings_lists = self.compute_list_of_savings_list(data)
solution_lengths = 0
processed_count = 0
for savings_list in savings_lists:
solution = MonteCarloSavingsSolution(data, vehicles)
for i, j in savings_list[:]:
if solution.is_complete():
break
if solution.can_process((i, j)):
solution, inserted = solution.process((i, j))
if time.time() - start > timeout:
break
if time.time() - start > timeout:
break
if solution.is_complete() and not best_feasible.is_complete():
best_feasible = solution
elif solution.is_complete() and solution.length() < best.length():
best_feasible = solution
if solution.length() < best.length():
best = solution
time_found = time.time() - start
processed_count = processed_count + 1
solution_lengths = solution_lengths + solution.length()
print 'best solution found on {}'.format(time_found)
if processed_count:
print 'average solution lengths: {}'.format(solution_lengths / float(processed_count))
if not best.is_complete():
from project import util
print 'Best solution not feasible, printing best feasible found'
util.print_solution(best_feasible)
return best
def solve(self, data, vehicles, timeout):
"""Solves the CVRP problem using Monte Carlo Savings method (OLIVEIRA, 2014)
Parameters:
data: CVRPData instance
vehicles: Vehicles number
timeout: max processing time in seconds
Returns a solution (MonteCarloSavingsSolution class))
"""
start = time.time()
time_found = None
best = MonteCarloSavingsSolution(data, vehicles)
best_feasible = best
savings_lists = self.compute_list_of_savings_list(data)
solution_lengths = 0
processed_count = 0
for savings_list in savings_lists:
solution = MonteCarloSavingsSolution(data, vehicles)
for i, j in savings_list[:]:
if solution.is_complete():
break
if solution.can_process((i, j)):
solution, inserted = solution.process((i, j))
if time.time() - start > timeout:
break
if time.time() - start > timeout:
break
if solution.is_complete() and not best_feasible.is_complete():
best_feasible = solution
elif solution.is_complete() and solution.length() < best.length():
best_feasible = solution
best = solution
time_found = time.time() - start
processed_count = processed_count + 1
solution_lengths = solution_lengths + solution.length()
print('best solution found after {} seconds'.format(time_found))
if processed_count:
print('average solution lengths: {}'.format(
solution_lengths / float(processed_count)))
if not best.is_complete():
from project import util
print('Best solution not feasible, printing best feasible found')
util.print_solution(best_feasible)
return best
def main():
if len(sys.argv) < 3: # python main.py <file> <vehicles_number> [<p>]
return usage()
input_file = sys.argv[1]
data = data_input.read_file(input_file)
vehicles = int(sys.argv[2])
lambda_p = None
if sys.argv == 4:
lambda_p = float(sys.argv[3])
clarke_wright_solver = clarke_wright.ClarkeWrightSolver()
monte_carlo_savings_solver = monte_carlo_savings.MonteCarloSavingsSolver(
lambda_p)
binary_mcscws_solver = binary_mcscws.BinaryMCSCWSSolver()
timeout = 300
algorithms = [(clarke_wright_solver, 'ClarkeWrightSolver'),
(monte_carlo_savings_solver, 'MonteCarloSavingsSolver'),
(binary_mcscws_solver, 'BinaryMCSCWSSolver')]
best_algorithm = None
best_solution = None
for solver, algorithm in algorithms:
print("=== Starting {} algorithm ===\n".format(algorithm))
start = time.time()
solution = solver.solve(data, vehicles, timeout)
elapsed = time.time() - start
if not solution.is_complete():
print('Solution from algorithm {} not a complete solution'.format(
algorithm))
print('{} solution:'.format(algorithm))
util.print_solution(solution)
print('Elapsed time (seconds): {}'.format(elapsed))
if best_algorithm is None:
best_algorithm = algorithm
if best_solution is None:
best_solution = solution
if solution.length() < best_solution.length():
best_solution = solution
best_algorithm = algorithm
print("")
print("=== Finished {} algorithm ===\n".format(algorithm))
print('Best solution for \"{}\" problem was from algorithm {}'.format(
input_file, best_algorithm))
def main():
if len(sys.argv) < 3: # python main.py <file> <vehicles_number> [<p>]
return usage()
data = data_input.read_file(sys.argv[1])
vehicles = int(sys.argv[2])
lambda_p = None
if sys.argv == 4:
lambda_p = float(sys.argv[3])
clarke_wright_solver = clarke_wright.ClarkeWrightSolver()
monte_carlo_savings_solver = monte_carlo_savings.MonteCarloSavingsSolver(lambda_p)
binary_mcscws_solver = binary_mcscws.BinaryMCSCWSSolver()
timeout = 300
algorithms = [
(clarke_wright_solver, 'ClarkeWrightSolver'),
(monte_carlo_savings_solver, 'MonteCarloSavingsSolver'),
(binary_mcscws_solver, 'BinaryMCSCWSSolver')
]
best_algorithm = None
best_solution = None
for solver, algorithm in algorithms:
start = time.time()
solution = solver.solve(data, vehicles, timeout)
elapsed = time.time() - start
if not solution.is_complete():
print 'Solution from algorithm {} not a complete solution'.format(algorithm)
print '{} solution:'.format(algorithm)
util.print_solution(solution)
print 'Elapsed time (seconds): {}'.format(elapsed)
if best_algorithm is None:
best_algorithm = algorithm
if best_solution is None:
best_solution = solution
if solution.length() < best_solution.length():
best_solution = solution
best_algorithm = algorithm
print
print 'Best: {}'.format(best_algorithm)
def main():
if len(sys.argv) < 2: # python main.py <file> <vehicles_number>
return usage()
data = data_input.read_file(sys.argv[1])
vehicles = 99
timeout = 300
#clarke_wright_solver = clarke_wright.ClarkeWrightSolver()
binary_mcscws_solver = binary_mcscws.BinaryMCSCWSSolver()
start = time.time()
solution = binary_mcscws_solver.solve(data, vehicles, timeout)
elapsed = time.time() - start
routeList = util.print_solution(solution)
print routeList
print 'Elapsed time (seconds): {}'.format(elapsed)