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Last active Nov 28, 2020
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TSP implementation for StackOverflow support
{'Dropped': [5, 8, 9, 20, 21, 22, 25, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41], 'Scheduled': [[0, 21600, 21600], [14, 21964, 21982], [15, 24235, 24253], [10, 26468, 26486], [12, 28494, 28512], [11, 30568, 30586], [13, 32677, 32695], [23, 34869, 34887], [29, 37037, 37055], [24, 39103, 39121], [7, 41413, 41431], [3, 43898, 43916], [2, 46266, 46284], [4, 48674, 48692], [6, 51672, 51690], [17, 53921, 53939], [19, 56163, 56181], [18, 58543, 58561], [26, 60553, 60571], [16, 62703, 62721], [1, 64800, 64800]]}
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
Matrix = 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Windows = [[21600, 21600], [64800, 64800], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400], [0, 86400]]
Durations = [0, 0, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800, 1800]
Penalties = [576460752303423487, 576460752303423487, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000, 100000]
Slack_Max = 86400
Capacity = 86400
# The inputs to RoutingIndexManager are:
# 1. The number of rows of the time matrix, which is the number of locations (including the depot).
# 2. The number of vehicles in the problem.
# 3. The node corresponding to the depot.
# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(len(Matrix), 1, [0], [1])
# Create Routing Model.
routing = pywrapcp.RoutingModel(manager)
# Create and register a transit callback.
def time_callback(from_index, to_index):
# Returns the travel time between the two nodes.
# Convert from routing variable Index to time matrix NodeIndex.
from_node = manager.IndexToNode(from_index)
to_node = manager.IndexToNode(to_index)
return Matrix[from_node][to_node] + Durations[from_node]
transit_callback_index = routing.RegisterTransitCallback(time_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
# Add Time Windows constraint.
routing.AddDimension(
transit_callback_index,
Slack_Max, # An upper bound for slack (the wait times at the locations).
Capacity, # An upper bound for the total time over each vehicle's route.
False, # Determine whether the cumulative variable is set to zero at the start of the vehicle's route.
'Time')
time_dimension = routing.GetDimensionOrDie('Time')
# Allow all locations except the first two to be droppable.
for node in range(2, len(Matrix)):
routing.AddDisjunction([manager.NodeToIndex(node)], Penalties[node])
# Add time window constraints for each location except depot.
for location_idx, time_window in enumerate(Windows):
if location_idx == 0 or location_idx == 1:
continue
index = manager.NodeToIndex(location_idx)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
# Add time window constraints for each vehicle start node.
index = routing.Start(0)
time_dimension.CumulVar(index).SetRange(Windows[0][0],Windows[0][1])
index = routing.End(0)
time_dimension.CumulVar(index).SetRange(Windows[1][0],Windows[1][1])
# Instantiate route start and end times to produce feasible times.
routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.Start(0)))
routing.AddVariableMinimizedByFinalizer(time_dimension.CumulVar(routing.End(0)))
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
# Setting local search metaheuristics:
search_parameters.local_search_metaheuristic = (routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
search_parameters.time_limit.seconds = 5
search_parameters.log_search = False
# Solve the problem.
solution = routing.SolveWithParameters(search_parameters)
# Print the results
result = {
'Dropped': [],
'Scheduled': []
}
# Return the dropped locations
for node in range(routing.Size()):
if routing.IsStart(node) or routing.IsEnd(node):
continue
if solution.Value(routing.NextVar(node)) == node:
result['Dropped'].append(manager.IndexToNode(node))
# Return the scheduled locations
time = 0
index = routing.Start(0)
while not routing.IsEnd(index):
time = time_dimension.CumulVar(index)
result['Scheduled'].append([manager.IndexToNode(index), solution.Min(time),solution.Max(time)])
index = solution.Value(routing.NextVar(index))
time = time_dimension.CumulVar(index)
result['Scheduled'].append([manager.IndexToNode(index), solution.Min(time),solution.Max(time)])
print(result)
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