问题描述
我正在研究一个有能力的车辆路线问题（参考 Google OR 工具）。
我的代码：
dist = distanceMetric.get_metric('haversine')
def create_data_model_cap_con(df_user,no_fos):
"""Stores the data for the problem."""
data = {}
data['distance_matrix'] = dist.pairwise(df_user [['lat','lon']].to_numpy())*6373
#a = dist.pairwise(df_user_org[['lat','lon']])
#a[:,0] = 0
#a[0,:] = 0
#data['starts'] = [2,5]
#data['ends'] = [0,0]
#assert(len(data['starts']) == len(data['ends']))
#assert(data['num_vehicles'] == len(data['starts']))
#A = dist.pairwise(df_user [['lat','lon']].to_numpy())*6373
#x = np.c_[np.zeros(len(A[0])),A ]
#y = np.vstack([np.zeros(len(x[0])),x])
#data['distance_matrix']=a
data['num_vehicles'] = no_fos
data['starts'] = [2,5,9,12]
data['ends'] = [0,0]
data['demands'] = df_user["no_cases"].to_list()
data['vehicle_capacities'] = [75]*no_fos
return data
def print_solution_cap_con(data,manager,routing,assignment):
"""Prints assignment on console."""
# display dropped nodes.
dropped_nodes = 'Dropped nodes:'
for node in range(routing.Size()):
if routing.Isstart(node) or routing.IsEnd(node):
continue
if assignment.Value(routing.Nextvar(node)) == node:
dropped_nodes += ' {}'.format(manager.IndexToNode(node))
print(dropped_nodes)
# display routes
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 distance_cap_con(x,y,data):
dis = data['distance_matrix'][x][y]
return dis
def get_routes_cap_con(solution,df_user,data):
"""Get vehicle routes from a solution and store them in an array."""
# Get vehicle routes and store them in a two dimensional array whose
# i,j entry is the jth location visited by vehicle i along its route.
routes = []
#routes_dist = []
for route_nbr in range(routing.vehicles()):
index = routing.Start(route_nbr)
route = [manager.IndexToNode(index)]
while not routing.IsEnd(index):
index = solution.Value(routing.Nextvar(index))
route.append(manager.IndexToNode(index))
routes.append(route)
#routes = get_routes(solution,manager)
routes_t = pd.DataFrame(routes).T
col_to_iter = routes_t.columns
routes_t['route_info'] = routes_t.index
routes_t = pd.melt(routes_t,id_vars=['route_info'],value_vars=col_to_iter)
routes_t = routes_t.drop_duplicates(subset='value',keep="first")
df_user['value'] = df_user.index
df_user_out = pd.merge(df_user,routes_t,on="value")
df_user_out = df_user_out.sort_values(by=['variable','route_info'])
df_user_out['route_lag'] = df_user_out.groupby('variable')['value'].shift(-1).fillna(0)
df_user_out['route_lag'] = df_user_out['route_lag'].astype(np.int64)
df_user_out['route_info'] = df_user_out['route_info'].astype(np.int64)
df_user_out['dist'] = df_user_out.apply(lambda row: distance_cap_con(row['route_lag'],row['value'],data),axis=1)
return df_user_out
def get_sol_cap_con(sub_dist_fil,fos_cnt,state_fil):
df_user_org_sub = df_user_org[(df_user_org.sub_district == sub_dist_fil) & (df_user_org.State == state_fil) ]
df_user_org_sub.reset_index( inplace=True,drop=True)
print(sub_dist_fil," no fos",fos_cnt)
fos_cnt=fos_cnt
data = create_data_model_cap_con(df_user_org_sub,fos_cnt)
# Create the routing index manager.
manager = pywrapcp.RoutingIndexManager(len(data['distance_matrix']),data['num_vehicles'],data['starts'],data['ends'])
# Create Routing Model.
routing = pywrapcp.RoutingModel(manager)
# Create and register a transit callback.
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['distance_matrix'][from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(distance_callback)
# Define cost of each arc.
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
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,# null capacity slack
data['vehicle_capacities'],# vehicle maximum capacities
True,# start cumul to zero
'Capacity')
# Allow to drop nodes.
penalty = 1000
for node in range(1,len(data['distance_matrix'])):
routing.Adddisjunction([manager.NodetoIndex(node)],penalty)
# Setting first solution heuristic.
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.time_limit.seconds = 30
search_parameters.solution_limit = 100
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEApest_ARC)
search_parameters.local_search_Metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GUIDED_LOCAL_SEARCH)
#search_parameters.time_limit.FromSeconds(30)
# Solve the problem.
solution = routing.solveWithParameters(search_parameters)
out_df = get_routes_cap_con(solution,df_user_org_sub,data)
# Print solution on console.
if solution:
print_solution_cap_con(data,solution)
return out_df
我的目标是使用这个有能力的车辆路线问题（google OR tools - https://developers.google.com/optimization/routing/routing_tasks），其中我已经指定了车辆可以承载的重量（[75] *no_fos）。现在我想设置车辆的初始节点（位置），它将等于我的数据集的索引号（我手动分配 data['starts'] = [2,12] 和数据['结束'] = 0）。但是在运行它时，即使我将时间限制设置为 30 秒，我的内核也会失去连接。它会在几秒钟内丢失/死亡，所以我无法获得任何输出。
如何修改上面的代码，以便我添加重量（车辆的容量）并根据我的意愿（索引号）设置车辆的初始节点（位置）而不是零（即我不想要数据['仓库'] = 0)？
我也愿意采用 Google OR 工具以外的任何工具，如果这有助于我解决问题。
解决方法
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