Using Capacity Constraint with Pickup and Delivery - python-3.x

Question

Being new to OR-Tools libraries I am unable to modify the existing code for my requirements. I have a requirement to add capacity constraint with pickup and delivery i.e a person will deliver items items as mentioned in pickup and delivery algo, but there will be a constraint how much items he can accommodate with him. I tried using code for capacity constraint with pickup and delivery code, but didn't get success. Here is a sample code:
from __future__ import print_function
from ortools.constraint_solver import routing_enums_pb2
from ortools.constraint_solver import pywrapcp
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
data['vehicle_capacities'] = [15,15,15,15]
data['demands'] = [0, 1, 1, 3, 6, 3, 6, 8, 8, 1, 2, 1, 2, 6, 6, 8, 8]
return data
def print_solution(data, manager, routing, assignment):
"""Prints assignment on console."""
total_distance = 0
total_load = 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
route_load = 0
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
route_load += data['demands'][node_index]
plan_output += ' {0} Load({1}) -> '.format(node_index, route_load)
previous_index = index
index = assignment.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(
previous_index, index, vehicle_id)
plan_output += ' {0} Load({1})\n'.format(manager.IndexToNode(index),
route_load)
plan_output += 'Distance of the route: {}m\n'.format(route_distance)
plan_output += 'Load of the route: {}\n'.format(route_load)
print(plan_output)
total_distance += route_distance
total_load += route_load
print('Total distance of all routes: {}m'.format(total_distance))
print('Total load of all routes: {}'.format(total_load))
def main():
"""Entry point of the program."""
# Instantiate the data problem.
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)
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# Add Capacity constraint.
def demand_callback(from_index):
"""Returns the demand of the node."""
# Convert from routing variable Index to demands NodeIndex.
from_node = manager.IndexToNode(from_index)
return data['demands'][from_node]
demand_callback_index = routing.RegisterUnaryTransitCallback(
demand_callback)
routing.AddDimensionWithVehicleCapacity(
demand_callback_index,
0, # null capacity slack
data['vehicle_capacities'], # vehicle maximum capacities
True, # start cumul to zero
'Capacity')
# Add Distance constraint.
dimension_name = 'Distance'
routing.AddDimension(
transit_callback_index,
0, # no slack
3000, # vehicle maximum travel distance
True, # start cumul to zero
dimension_name)
distance_dimension = routing.GetDimensionOrDie(dimension_name)
distance_dimension.SetGlobalSpanCostCoefficient(100)
# 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)
# Solve the problem.
assignment = routing.SolveWithParameters(search_parameters)
print(assignment)
# Print solution on console.
if assignment:
print("1")
print_solution(data, manager, routing, assignment)
if __name__ == '__main__':
main()

Answer 1

There is no solution because the total demand is greater than the total vehicle capacity. The demand is 70 the capacity is 60.

Answer 2

For anyone having trouble getting a solution to this problem, I'll just leave it here instead of creating a new question and writing the solution myself. There might be something in this answer you might've missed.
Lets first understand how capacity constraint works in plain English
What is the demands array?
It is a unit of quantity which the vehicle will have to carry upon going to that location.
What is the vehicle_capacities array?
It is the maximum quantity in units which a particular vehicle can carry.
Now moving on to pickup & delivery
In this case however, our 'vehicles' don't carry an additional weight/quantity (in units) upon reaching the delivery location. Instead, it will release the quantity (in units) which it carried from the pickup location.
So, our demands array will change accordingly.
Considering this is our pickup_deliveries array
data['pickups_deliveries'] = [
[1, 6],
[2, 10],
[4, 3],
[5, 9],
[7, 8],
[15, 11],
[13, 12],
[16, 14],
]
Our demands array should look something like:
data['demands'] = [0, 1, 2, -6, 6, 10, -1, 8, -8, -10, -2, -9, -4, 4, -13, 9, 13]
Where each delivery location will release that same amount of quantity which we picked up from the pickup location.
Side Note (Not related to question but might help you)
When using pickup and delivery with capacity constraint or combining it with any other constraint like time window or even multiple start end, it makes the job a whole lot easier if we first specify depots and then pickup1 then drop1, then pickup2 then drop2, etc.
Eg:
data['num_vehicles'] = 4
# put all vehicles at the start of your 'addresses' array (i.e. they will be the first rows in the distance / time matrices)
data['starts'] = [0, 1, 2, 3]
data['ends'] = [0, 1, 2, 3]
# then simply, from the next 2 indices start defining the pickups and drops
# i.e. 4 is pickup1 5 is drop1, 6 is pickup2 7 is drop2, etc. (this also makes it easier for dynamic input)
# i.e. if num_vehicles is 3 -> next 2 -> 3,4 (since index starts from 0)
data['pickups_deliveries'] = [
[4, 5],
[6, 7],
[8, 9],
[10, 11],
[12, 13],
[14, 15]
]
# then 0 to num_vehicle values will be 0 (since depots won't have weight in normal condition unless you have something...)
# and the rest of the numbers will be pairs of pickups and drop weights i.e. what is picked up is dropped so (num,-num) for 1 pickup-drop pair etc.
data['demands'] = [0, 0, 0, 0, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1]
data['vehicle_capacities'] = [1, 1, 1, 1] # this depends on your problem.
# here I have considered that a vehicle can only carry one thing at a time