### Python 实现车机协同配送解决方案 #### 数据准备与预处理 为了构建有效的协同配送模型，需先准备好公交站点和需求点的数据。这涉及读取并解析地理坐标数据，以便后续的距离计算和其他操作[^1]。 ```python import pandas as pd # 假设csv文件中含有'latitude', 'longitude' bus_stops_df = pd.read_csv('path_to_bus_stops.csv') demand_points_df = pd.read_csv('path_to_demand_points.csv') print(bus_stops_df.head()) print(demand_points_df.head()) ``` #### 计算距离矩阵 基于上述准备好的位置信息，可以采用Haversine公式或其他适当的方法来计算各站点间以及它们同需求点间的直线距离或实际道路网上的行驶距离。 ```python from math import radians, sin, cos, sqrt, atan2 def haversine(lat1, lon1, lat2, lon2): R = 6371e3 # 地球半径单位米 phi1 = radians(lat1) phi2 = radians(lat2) delta_phi = radians(lat2 - lat1) delta_lambda = radians(lon2 - lon1) a = (sin(delta_phi / 2)**2 + cos(phi1) * cos(phi2) * sin(delta_lambda / 2)**2) c = 2 * atan2(sqrt(a), sqrt(1-a)) d = R * c # 距离单位为米 return d distance_matrix = [] for _, stop_row in bus_stops_df.iterrows(): distances_from_stop = [ haversine(stop_row['latitude'], stop_row['longitude'], demand_point['latitude'], demand_point['longitude']) for index, demand_point in demand_points_df.iterrows() ] distance_matrix.append(distances_from_stop) distances_df = pd.DataFrame(distance_matrix, columns=demand_points_df.index.values.tolist(), index=bus_stops_df.index.values.tolist()) print(distances_df.head()) ``` #### 构建优化模型 针对特定的应用场景——即一辆卡车搭配两架无人机进行包裹递送的任务，可以通过定义目标函数（比如最小化总行程长度）、约束条件（如载重限制、飞行范围等），进而应用遗传算法寻找近似最优解[^2]。 ```python import numpy as np from deap import base, creator, tools, algorithms creator.create("FitnessMin", base.Fitness, weights=(-1.0,)) creator.create("Individual", list, fitness=creator.FitnessMin) toolbox = base.Toolbox() # 参数设置 num_generations = 50 population_size = 100 crossover_probability = 0.8 mutation_probability = 0.2 # 初始化种群个体编码方式及边界设定 gene_length = len(distances_df.columns)*len(distances_df.index)+2 # 加上起始结束节点 min_val, max_val = 0, gene_length-1 toolbox.register("attr_float", lambda: np.random.randint(min_val, max_val)) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_float, n=gene_length) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evaluate(individual): """评估适应度""" total_distance = sum([distances_df.iloc[int(i//len(distances_df)), int(i%len(distances_df))] for i in individual]) return total_distance, toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutShuffleIndexes, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) toolbox.register("evaluate", evaluate) pop = toolbox.population(n=population_size) resulting_population, logbook = algorithms.eaSimple(population=pop, toolbox=toolbox, cxpb=crossover_probability, mutpb=mutation_probability, ngen=num_generations, verbose=True) best_solution = tools.selBest(resulting_population, k=1)[0] print(f"最佳路径顺序:{best_solution}") print(f"对应最短路程:{evaluate(best_solution)}") ``` 此部分代码展示了如何通过遗传算法框架`DEAP`库来进行简单的进化过程模拟，在此基础上可根据具体业务逻辑调整参数配置或者引入更复杂的策略以提高求解效率和质量。 #### 结果分析与可视化 最后一步是对得到的结果做进一步解释说明，并可能借助图表工具直观展示出来，帮助理解最终形成的配送路线安排情况。 ```python import matplotlib.pyplot as plt plt.figure(figsize=(10, 6)) for idx in best_solution[:-1]: start_idx = divmod(idx, len(distances_df))[::-1] end_idx = divmod(best_solution[best_solution.index(idx)+1], len(distances_df))[::-1] plt.plot((distances_df.columns[start_idx[0]], distances_df.columns[end_idx[0]]), (distances_df.index[start_idx[1]], distances_df.index[end_idx[1]]), marker='o') plt.title('Optimized Delivery Route Visualization') plt.xlabel('Demand Points Index') plt.ylabel('Bus Stops Index') plt.grid(True) plt.show() ```