def draw_route_graph(
data: dict,
routing: RoutingModel,
manager: RoutingIndexManager,
assignment: Assignment,
filename: str = 'route.png',
prog='sfdp',
) -> None:
"""
Draw a route graph based on the solution of the problem.
"""
weights = data['weights']
demands = data['demands']
time_windows = data['time_windows']
graph = pgv.AGraph(directed=True)
def _node(index: int) -> str:
if index == 0:
return f'{index}\nDepot'
return f'{index}\nDemand: {demands[index]}\nRange: {time_windows[index]}'
for vehicle_id in range(data['num_vehicles']):
index = routing.Start(vehicle_id)
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
next_index = assignment.Value(routing.NextVar(index))
next_node_index = manager.IndexToNode(next_index)
weight = weights[node_index][next_node_index]
graph.add_edge(_node(node_index), _node(next_node_index), weight=weight, label=weight)
index = next_index
graph.draw(filename, prog=prog)
print(f'The route graph has been saved to {filename}.')
def _set_capacity_dimension_constraints(
self, data: VehicleRoutingProblemInstance, routing: RoutingModel,
manager: RoutingIndexManager):
if data.courier_capacities is None:
return
def demand_callback(index):
node = manager.IndexToNode(index)
return data.node_demands[node]
capacity_callback = routing.RegisterUnaryTransitCallback(
demand_callback)
routing.AddDimensionWithVehicleCapacity(
capacity_callback,
0, # waiting time
data.courier_capacities, # maximum distance per vehicle
False, # Force start cumul to zero.
self.CAPACITY_DIMENSION_NAME)
capacity_dimension = routing.GetDimensionOrDie(
self.CAPACITY_DIMENSION_NAME)
for courier_idx, start_utilization in enumerate(
data.start_utilizations):
node_idx = routing.Start(courier_idx)
self._set_constraint_on_var(
node_idx,
TimeWindowConstraint(node=node_idx,
is_hard=True,
from_time=start_utilization,
to_time=start_utilization),
capacity_dimension)
def print_solution(
data: dict,
routing: RoutingModel,
manager: RoutingIndexManager,
assignment: Assignment,
) -> None:
"""
Print the solution.
"""
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = routing.GetDimensionOrDie('Time')
total_time = 0
for vehicle_id in range(data['num_vehicles']):
index = routing.Start(vehicle_id)
node_props = []
while not routing.IsEnd(index):
props = node_properties(manager, assignment, capacity_dimension, time_dimension, index)
node_props.append(props)
index = assignment.Value(routing.NextVar(index))
props = node_properties(manager, assignment, capacity_dimension, time_dimension, index)
node_props.append(props)
route_time = assignment.Value(time_dimension.CumulVar(index))
route = "\n -> ".join(['[Node %2s: Load(%s) Time(%2s, %s)]' % prop for prop in node_props])
plan_output = (f'Route for vehicle {vehicle_id}:\n {route}\n'
f'Load of the route: {props[1]}\nTime of the route: {route_time} min\n')
print(plan_output)
total_time += route_time
print(f'Total time of all routes: {total_time} min')
def print_solution(data: VehicleRoutingProblemInstance,
manager: RoutingIndexManager, routing: RoutingModel,
solution: Assignment):
"""Prints assignment on console."""
time_dimension = routing.GetDimensionOrDie('Duration')
total_time = 0
for vehicle_id in range(data.num_plans_to_create):
start_time = None
index = routing.Start(vehicle_id)
plan_output = 'Route for vehicle {}:\n'.format(vehicle_id)
while not routing.IsEnd(index):
time_var = time_dimension.CumulVar(index)
if start_time is None:
start_time = solution.Min(time_var)
plan_output += '{0} Time({1},{2}) -> '.format(
manager.IndexToNode(index), solution.Min(time_var),
solution.Max(time_var))
index = solution.Value(routing.NextVar(index))
time_var = time_dimension.CumulVar(index)
plan_output += '{0} Time({1},{2})\n'.format(
manager.IndexToNode(index), solution.Min(time_var),
solution.Max(time_var))
plan_output += f'Start of the route: {start_time}\n'
plan_output += 'Time of the route: {} sec\n'.format(
solution.Min(time_var) - start_time)
print(plan_output)
total_time += solution.Min(time_var) - start_time
print('Total time of all routes: {} sec'.format(total_time))
def from_cvrp_solution(cls, data_model: DataModel,
manager: pywrapcp.RoutingIndexManager,
routing: pywrapcp.RoutingModel,
assignment) -> 'Optional[List[VehicleRoute]]':
if not assignment:
return None
res = [] # type: List[VehicleRoute]
for vehicle_id in range(data_model.vehicle_number):
index = routing.Start(vehicle_id)
route_indices = [] # type: List[int]
route_distance = 0 # float
route_load = 0 # float
while not routing.IsEnd(index):
node_index = manager.IndexToNode(index)
route_indices.append(node_index)
route_load += data_model.demands[node_index]
previous_index = index
index = assignment.Value(routing.NextVar(index))
route_distance += routing.GetArcCostForVehicle(
previous_index, index, vehicle_id)
route_indices.append(0) # returns to depot
res.append(
cls(data_model, vehicle_id, route_indices, route_distance,
route_load))
current_app.logger.debug('CVRP Solution:\n{res}')
return res
def apply(self, manager: pywrapcp.RoutingModel, routing: pywrapcp.RoutingModel, data_model: DataModel):
def distance_callback(from_index, to_index):
"""Returns the 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_model.distance_matrix[from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
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)
for i in range(data_model.number_of_engineers):
routing.AddVariableMinimizedByFinalizer(
distance_dimension.CumulVar(routing.Start(i)))
routing.AddVariableMinimizedByFinalizer(
distance_dimension.CumulVar(routing.End(i)))
return routing
def print_solution(
data: dict,
routing: RoutingModel,
manager: RoutingIndexManager,
assignment: Assignment,
) -> None:
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = None
total_cost = 0
if 'time_windows' in data.keys():
time_dimension = routing.GetDimensionOrDie('Time')
total_time = 0
for vehicle_id in range(data['num_vehicles']):
index = routing.Start(vehicle_id)
node_props = []
while not routing.IsEnd(index):
props = node_properties(data, manager, assignment, capacity_dimension, time_dimension, index)
node_props.append(props)
index = assignment.Value(routing.NextVar(index))
props = node_properties(data, manager, assignment, capacity_dimension, time_dimension, index)
node_props.append(props)
# arc weight
cost = 0
node_indexes = [prop[0] for prop in node_props]
for i in range(len(node_indexes)-1):
idx_from = node_indexes[i]
idx_to = node_indexes[i+1]
cost += data['dist_mat'][idx_from][idx_to]
total_cost += cost
if 'time_windows' in data.keys():
route_time = assignment.Value(time_dimension.CumulVar(index))
route = "\n -> ".join(['[Node %2s(%s)TW(%2s, %2s): Vehicle Load(%2s) and Arrived(%2s)]' % prop for prop in node_props])
plan_output = (f'Route for vehicle {vehicle_id}:\n {route}\n'
f'Cost: {cost}\nLoad: {props[-1]}\nTime: {route_time} min\n')
print(plan_output)
total_time += route_time
else:
route = "\n -> ".join(['[Node %2s(%s): Vehicle Load(%2s)]' % prop for prop in node_props])
plan_output = (f'Route for vehicle {vehicle_id}:\n {route}\n'
f'Cost: {cost}\nLoad: {props[-1]}\n')
print(plan_output)
print(f'Total cost of all routes: {total_cost}\n')
return total_cost
if 'time_windows' in data.keys():
print('*** format ***: \n[Node node_index(demand)TW(Time Window min, max): Vehicle Load(accumulated load) and Arrived(time)]')
print(f'Total time of all routes: {total_time} min')
def _set_time_dimension_constraints(
self, routing: RoutingModel, manager: RoutingIndexManager,
dimension_name: str,
start_time_windows: List[TimeWindowConstraint],
node_time_windows: List[TimeWindowConstraint],
num_plans_to_create: int):
# ========= TIME WINDOW =========
time_dimension = routing.GetDimensionOrDie(dimension_name)
for constraint in start_time_windows:
index = routing.Start(constraint.node)
self._set_constraint_on_var(index, constraint, time_dimension)
for constraint in node_time_windows:
index = manager.NodeToIndex(constraint.node)
self._set_constraint_on_var(index, constraint, time_dimension)
# Instantiate route start and end times to produce feasible times.
for i in range(num_plans_to_create):
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.Start(i)))
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.End(i)))
def print_solution(data: dict, routing: pywrapcp.RoutingModel,
assignment: pywrapcp.Assignment):
"""
Print routes on console.
"""
capacity_dimension = routing.GetDimensionOrDie('Capacity')
time_dimension = routing.GetDimensionOrDie('Time')
total_time = 0
d = {}
r = []
for vehicle_id in range(data['num_vehicles']):
index = routing.Start(vehicle_id)
node_props = []
while not routing.IsEnd(index):
props = node_properties(routing, assignment, capacity_dimension,
time_dimension, index)
node_props.append(props)
index = assignment.Value(routing.NextVar(index))
props = node_properties(routing, assignment, capacity_dimension,
time_dimension, index)
node_props.append(props)
route_time = assignment.Value(time_dimension.CumulVar(index))
route = "\n -> ".join(['[Node %2s: Load(%s) Time(%2s, %s)]' % prop \
for prop in node_props])
plan_output = f'Route for vehicle {vehicle_id}:\n {route}\n' + \
f'Load of the route: {props[1]}\nTime of the route: {route_time} min\n'
# print(plan_output)
#convert to dict
ddd = []
for node in node_props:
dd = {"node": node[0], "load": node[1], "time": [node[2], node[3]]}
ddd.append(dd)
r.append({
"vehicle_id": vehicle_id,
"route": ddd,
"total_time": route_time,
"load": len(ddd) - 2
})
total_time += route_time
d['solver'] = r
d['total_time'] = total_time
# print(f'Total time of all routes: {total_time} min')
return d
def _extract_solution(self, assignment: Assignment, vrp_instance: VehicleRoutingProblemInstance,
manager: RoutingIndexManager, routing: RoutingModel) \
-> VehicleRoutingProblemSolution:
routes = []
etas = []
etds = []
time_dimension = routing.GetDimensionOrDie(
self.DURATION_DIMENSION_NAME)
for vehicle_id in range(vrp_instance.num_plans_to_create):
index = routing.Start(vehicle_id)
route = []
route_etas = []
route_etds = []
while not routing.IsEnd(index):
node = manager.IndexToNode(index)
if node in vrp_instance.pickup_nodes:
this_event_time = vrp_instance.pickup_service_time
elif node in vrp_instance.drop_nodes:
this_event_time = vrp_instance.drop_service_time
else:
this_event_time = 0
time_var = time_dimension.CumulVar(index)
eta = assignment.Min(time_var)
etd = assignment.Max(time_var) + this_event_time
route.append(node)
route_etas.append(eta)
route_etds.append(etd)
index = assignment.Value(routing.NextVar(index))
routes.append(route)
etas.append(route_etas)
etds.append(route_etds)
ret = VehicleRoutingProblemSolution(plans=routes, etas=etas, etds=etds)
return ret
def _extract_solution(self, manager: RoutingIndexManager,
routing: RoutingModel, assignment: Assignment,
indices_to_visit: List[int]) -> Dict[str, Any]:
"""Transform results to a usable format
Parameters
----------
manager : RoutingIndexManager
OR-tools' object to manage conversion between NodeIndex and variable index
routing : RoutingModel
OR-tools' object for route solving
assignment : Assignment
OR-tools' object for mapping from variable to domains
indices_to_visit : List[int]
The list of indices corresponding to the desired facilities to visit
Returns
-------
Dict[str, Any]
With keys:
- 'objective', set to objective value (minified distance)
- 'order', instructions for the order of facilities to visit
"""
sln = {"objective": assignment.ObjectiveValue()}
stop_indices = []
index = routing.Start(0)
while not routing.IsEnd(index):
relative_index = manager.IndexToNode(index)
stop_indices.append(indices_to_visit[relative_index])
previous_index = index
index = assignment.Value(routing.NextVar(index))
relative_index = manager.IndexToNode(index)
stop_indices.append(indices_to_visit[relative_index])
sln["order"] = stop_indices
return sln
