分体配送车辆路径问题的最优路径

Ruey-Maw Chen, Jia-Ci Guo
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引用次数: 1

摘要

分割配送车辆路径问题(SDVRP)是将客户的需求分配到不同的车辆上进行服务。本研究将SDVRP分为两个阶段进行解决。第一阶段,采用粒子群算法(PSO)将客户划分为可分客户群和不可分客户群;然后利用另一种粒子群算法求解不可分割客户的车辆路径问题,并利用设计的最近插入启发式算法将可分割客户插入到VRP中的适当位置。同时,采用具有初始启发式速率的扫描算法提供不同的粒子群初始解,以提高第二阶段的效率。为了验证该方法的有效性,应用j.m. Belenguer给出的SDVRP问题集进行了测试。实验结果表明,所提方法的平均偏差均在10%以内。重申,本文提出的方案能够有效地解决SDVRP优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Routing for Split Delivery Vehicle Routing Problems
Split Delivery Vehicle Routing Problem (SDVRP) distributes the customers' demand to different vehicles for service. This study divides SDVRP into two stages for solutions. On stage one, Particle Swarm Optimization (PSO) was applied to separate customers into two bases, divisible and indivisible customer bases; and then another PSO was applied to resolve the Vehicle Routing Problem (VRP) of indivisible customers and a designed nearest insertion heuristic is utilized to insert the divisible customers into the proper location in the VRP. Meanwhile, a sweep algorithm with an initial heuristic rate are used to provide diverse PSO initial solutions so as to improve the efficiency on stage two. To verify the efficiency of the proposed method, SDVRP problem set given by J. M. Belenguer was applied for tests. The suggest methods came up with the experimental results shows that average deviations for the test instances are all within 10%. Restated, the proposed scheme of this work is able to resolve SDVRP optimization effectively.
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