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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination
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Novel Encoding and Routing Balance Insertion Based Particle Swarm Optimization with Application to Optimal CVRP Depot Location Determination

机译：基于编码和路由平衡插入的新型粒子群算法及其在最优CVRP仓库选址中的应用

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摘要

A depot location has a significant effect on the transportation cost in vehicle routing problems. This study proposes a hierarchical particle swarm optimization (PSO) including inner and outer layers to obtain the best location to establish a depot and the corresponding optimal vehicle routes using the determined depot location. The inner layer PSO is applied to obtain optimal vehicle routes while the outer layer PSO is to acquire the depot location. A novel particle encoding is suggested for the inner layer PSO, the novel PSO encoding facilitates solving the customer assignment and the visiting order determination simultaneously to greatly lower processing efforts and hence reduce the computation complexity. Meanwhile, a routing balance insertion (RBI) local search is designed to improve the solution quality. The RBI local search moves the nearest customer from the longest route to the shortest route to reduce the travel distance. Vehicle routing problems from an operation research library were tested and an average of 16% total routing distance improvement between having and not having planned the optimal depot locations is obtained. A real world case for finding the new plant location was also conducted and significantly reduced the cost by about 29%.
机译：在车辆选路问题中，仓库位置对运输成本有重大影响。这项研究提出了一个分层的粒子群优化算法（PSO），该算法包括内层和外层，以获取建立仓库的最佳位置以及使用确定的仓库位置的相应最佳车辆路线。内层PSO用于获得最佳的车辆路线，而外层PSO用于获取仓库位置。对于内层PSO，提出了一种新颖的粒子编码，新颖的PSO编码有助于同时解决客户分配和访问顺序确定，从而大大降低了处理工作量，从而降低了计算复杂度。同时，路由平衡插入（RBI）本地搜索旨在提高解决方案质量。 RBI本地搜索可将最近的客户从最长的路线移至最短的路线，以缩短行驶距离。测试了运营研究库中的车辆路线问题，并且在计划和不计划最佳仓库位置之间的平均总路线距离提高了16％。还进行了寻找新工厂位置的实际案例，并将成本降低了约29％。

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