Enforce delivery_a before next pickup (pickup_b) in a PDP #3787
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Hi, How can I force the vehicle to complete one delivery before heading to the pickup of the next order?
def create_data_model():
"""Stores the data for the problem."""
data = {}
data['distance_matrix'] = [
[
0, 548, 776, 696, 582, 274, 502, 194, 308, 194, 536, 502, 388, 354,
468, 776, 662
],
[
548, 0, 684, 308, 194, 502, 730, 354, 696, 742, 1084, 594, 480, 674,
1016, 868, 1210
],
[
776, 684, 0, 992, 878, 502, 274, 810, 468, 742, 400, 1278, 1164,
1130, 788, 1552, 754
],
[
696, 308, 992, 0, 114, 650, 878, 502, 844, 890, 1232, 514, 628, 822,
1164, 560, 1358
],
[
582, 194, 878, 114, 0, 536, 764, 388, 730, 776, 1118, 400, 514, 708,
1050, 674, 1244
],
[
274, 502, 502, 650, 536, 0, 228, 308, 194, 240, 582, 776, 662, 628,
514, 1050, 708
],
[
502, 730, 274, 878, 764, 228, 0, 536, 194, 468, 354, 1004, 890, 856,
514, 1278, 480
],
[
194, 354, 810, 502, 388, 308, 536, 0, 342, 388, 730, 468, 354, 320,
662, 742, 856
],
[
308, 696, 468, 844, 730, 194, 194, 342, 0, 274, 388, 810, 696, 662,
320, 1084, 514
],
[
194, 742, 742, 890, 776, 240, 468, 388, 274, 0, 342, 536, 422, 388,
274, 810, 468
],
[
536, 1084, 400, 1232, 1118, 582, 354, 730, 388, 342, 0, 878, 764,
730, 388, 1152, 354
],
[
502, 594, 1278, 514, 400, 776, 1004, 468, 810, 536, 878, 0, 114,
308, 650, 274, 844
],
[
388, 480, 1164, 628, 514, 662, 890, 354, 696, 422, 764, 114, 0, 194,
536, 388, 730
],
[
354, 674, 1130, 822, 708, 628, 856, 320, 662, 388, 730, 308, 194, 0,
342, 422, 536
],
[
468, 1016, 788, 1164, 1050, 514, 514, 662, 320, 274, 388, 650, 536,
342, 0, 764, 194
],
[
776, 868, 1552, 560, 674, 1050, 1278, 742, 1084, 810, 1152, 274,
388, 422, 764, 0, 798
],
[
662, 1210, 754, 1358, 1244, 708, 480, 856, 514, 468, 354, 844, 730,
536, 194, 798, 0
],
]
data['pickups_deliveries'] = [
[1, 6],
[2, 10],
[4, 3],
[5, 9],
[7, 8],
[15, 11],
[13, 12],
[16, 14],
]
data['num_vehicles'] = 4
data['depot'] = 0
return data def print_solution(data, manager, routing, solution):
"""Prints solution on console."""
print(f'Objective: {solution.ObjectiveValue()}')
total_distance = 0
for vehicle_id in range(data['num_vehicles']):
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
route_distance = 0
while not routing.IsEnd(index):
plan_output += ' {} -> '.format(manager.IndexToNode(index))
previous_index = index
index = solution.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(
previous_index, index, vehicle_id)
plan_output += '{}\n'.format(manager.IndexToNode(index))
plan_output += 'Distance of the route: {}m\n'.format(route_distance)
print(plan_output)
total_distance += route_distance
print('Total Distance of all routes: {}m'.format(total_distance)) def main():
# data = generate_data_model()
data = create_data_model()
# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),
data['num_vehicles'], data['depot'])
# Create Routing Model.
routing = pywrapcp.RoutingModel(manager)
# Define cost of each arc.
def distance_callback(from_index, to_index):
"""Returns the manhattan distance between the two nodes."""
# Convert from routing variable Index to distance matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return data['distance_matrix'][from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# Add Distance constraint.
dimension_name = 'Distance'
routing.AddDimension(
transit_callback_index,
0, # no slack
40000, # vehicle maximum travel distance
True, # start cumul to zero
dimension_name)
distance_dimension = routing.GetDimensionOrDie(dimension_name)
distance_dimension.SetGlobalSpanCostCoefficient(1000) # Change this, compute time
# Define Transportation Requests.
for request in data['pickups_deliveries']:
pickup_index = manager.NodeToIndex(request[0])
delivery_index = manager.NodeToIndex(request[1])
routing.AddPickupAndDelivery(pickup_index, delivery_index)
routing.solver().Add(
routing.VehicleVar(pickup_index) == routing.VehicleVar(
delivery_index))
routing.solver().Add(
distance_dimension.CumulVar(pickup_index) <=
distance_dimension.CumulVar(delivery_index))
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PARALLEL_CHEAPEST_INSERTION)
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_parameters.time_limit.FromSeconds(1)
# Solve the problem.
solution = routing.SolveWithParameters(search_parameters)
# Print solution on console.
if solution:
print_solution(data, manager, routing, solution) I have the following output objective: 2039304
Route for vehicle 0:
0 -> 5 -> 2 -> 10 -> 9 -> 0
Distance of the route: 1712m
Route for vehicle 1:
0 -> 7 -> 1 -> 6 -> 8 -> 0
Distance of the route: 1780m
Route for vehicle 2:
0 -> 16 -> 14 -> 13 -> 12 -> 0
Distance of the route: 1780m
Route for vehicle 3:
0 -> 4 -> 3 -> 15 -> 11 -> 0
Distance of the route: 2032m
Total Distance of all routes: 7304m |
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Replies: 2 comments 2 replies
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In your case i would go for an extra dimension of capacity 1, Note: I don't think playing with P&D policy will work in your case since you want to avoid |
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Thanks @Mizux for that, works really great! I realized that I could as well remove the P&D definition to make both the pickups and dropoffs dynamic. |
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In your case i would go for an extra dimension of capacity 1,
pickup +1, delivery -1. So vehicle must deliver first to be able to pickup the next.
Note: I don't think playing with P&D policy will work in your case since you want to avoid
P1, P2, D2, D1
(LIFO, stack) orP1, P2, D1, D2
(FIFO, queue).ref: https://github.com/google/or-tools/blob/main/ortools/constraint_solver/docs/PDP.md