质量体积双约束下车辆装载与配送路径联合优化研究

Joint Optimization of Vehicle Loading and Distribution Routes under Dual Constraints of Quality and Volume

中文关键词: 路径优化  遗传算法  三维装载  基于块的启发式算法

英文关键词: route optimization  genetic algorithm  3D loading  block based heuristic algorithm

中文摘要:

      目的 针对质量与体积共同限制的配送路径问题，综合考虑订单不可拆分、货物的体积等约束，构建包含路径最短和装载率最高双目标的车辆装载与配送路径联合优化模型。方法 在车辆路径优化模型的求解方面，首先利用聚类算法对配送区域进行划分，然后通过车辆的载质量判断是否能进行站点货物的配送，最后利用遗传算法求得最优路径。在三维装载模型的求解上使用贪心算法和基于块的启发式算法，解决了货物的装箱问题。结果 基于某公司具体实例对模型与算法的可行性进行了验证，优化后配送的车辆减少了1辆，配送距离减少了154.247 km，平均装载率达到了93.89%，节省了企业的配送成本。结论 所构建的模型以及求解的算法可以提高装载率和配送效率，为解决车辆装载与配送路径联合优化问题提供理论依据。

英文摘要:

      The work aims to constructa joint optimization model for vehicle loading and distribution routes with the shortest route and the highest loading rate, taking into account constraints such as the indivisibility of orders and the volume of goods, so as to address the delivery route problem with common limitations of quality and volume. In terms of solving the vehicle route optimization model, first the clustering algorithm was used to partition the distribution area, and then the load capacity of the vehicle was used to determine whether the station goods could be delivered. Finally, the genetic algorithm was used to obtain the optimal route. The greedy algorithm and the block based heuristic algorithm were used to solve the loading problem of goods in the 3D loading model. The feasibility of the model and the algorithm was verified based on a specific example of a company. After optimization, the number of vehicles for delivery was reduced by 1, the delivery distance was reduced by 154.247 km, and the average loading rate reached 93.89%, saving the company's delivery costs. The results indicate that the constructed model and the solved algorithm can improve loading rate and delivery efficiency, providing a theoretical basis for solving the joint optimization problem of vehicle loading and distribution routes.