c101数据集vrp

### C101 数据集用于车辆路径问题(VRP) 对于涉及C101数据集的车辆路径问题(VRP)，该特定的数据集属于Solomon benchmark的一部分，被广泛应用于评估解决VRP算法的有效性[^1]。此数据集包含了客户位置、需求量以及服务时间窗等信息，旨在模拟现实世界中的配送挑战。 针对C101数据集设计的解决方案通常会考虑以下几个方面： - **客户需求分析**：理解每个客户的地理位置和服务要求，包括货物数量及时段限制。 - **优化目标设定**：确定主要追求的目标函数，比如最小化总行驶距离或成本、减少使用的车辆数目等。 - **求解方法选取**：采用精确算法（如分支定界法）、启发式算法或是元启发式算法来找到近似最优解。 下面是一个简单的Python实现例子，利用`ortools`库处理基于C101数据集的VRP问题: ```python 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 = {} # Load actual distance matrix, demands, etc., from C101 dataset here. data['distance_matrix'] = [ [0, 548, 776], [548, 0, 696], [776, 696, 0] ] # Simplified example; real distances should come from C101 file. data['demands'] = [0, 1, 1] data['vehicle_capacities'] = [2] data['num_vehicles'] = 1 data['depot'] = 0 return data def print_solution(data, manager, routing, solution): """Prints solution 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 = solution.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(): """Solve the CVRP problem with time windows using OR-tools library""" # 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) # Define cost function for each arc. 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') # Setting first solution heuristic. search_parameters = pywrapcp.DefaultRoutingSearchParameters() search_parameters.first_solution_strategy = ( routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC) # Solve the problem. solution = routing.SolveWithParameters(search_parameters) # Print solution on console. if solution: print_solution(data, manager, routing, solution) if __name__ == '__main__': main() ```