张异.包装物回收物流中的车辆路径优化问题[J].包装工程,2017,38(17):233-238.
ZHANG Yi.Vehicle Routing Optimization Problem in Package Recycling Logistics[J].Packaging Engineering,2017,38(17):233-238. |
| 包装物回收物流中的车辆路径优化问题 |
| Vehicle Routing Optimization Problem in Package Recycling Logistics |
| 投稿时间：2016-12-20 修订日期：2017-09-10 |
| DOI： |
| 中文关键词: 包装物回收 路径优化 遗传算法 蜜蜂进化机制 |
| 英文关键词: package recycling routing optimization genetic algorithm bee evolutionary mechanism |
| 基金项目:重庆市教委人文社科项目（16SKGH209）；重庆工商职业学院重点项目（ZD2014-03） |
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| 中文摘要: |
| 目的 提高遗传算法（GA）求解包装物回收车辆路径优化问题的性能。方法 通过对传统GA算法的改进，提出混合蜂群遗传算法（HBGA）。首先改进传统GA算法的初始种群生成方式，设计初始种群混合生成算子；其次，提出最大保留交叉算子，对优秀子路径进行保护；然后，在上述改进的基础上引入蜜蜂进化机制，用以保证种群多样性和优秀个体特征信息的利用程度；最后，对标准算例集进行仿真测试。结果 与传统GA算法相比，HBGA算法在全局寻优能力、算法稳定性和运行速度方面均有所改善。HBGA算法的全局寻优能力和算法稳定性均优于粒子群算法（PSO）、蚁群算法（ACO）和禁忌搜索算法（TS），但运行速度稍慢于TS算法。结论 对传统GA算法的改进是合理的，且HBGA算法整体求解性能优于PSO算法、ACO算法和TS算法。 |
| 英文摘要: |
| The work aims to improve the performance of genetic algorithm (GA) to solve the vehicle routing optimization problem in the package recycling. Based on the improvement of traditional genetic algorithm, the hybrid bee genetic algorithm (HBGA) was put forward. Firstly, the initial population generation method of the traditional GA was improved, and the mixed generation operator of the initial population was designed; secondly, the maximum reservation crossover operator was proposed to protect the excellent sub-path; then, on the basis of the above-mentioned improvement, the bee evolutionary mechanism was introduced to ensure thepopulation diversity and the utilization of the excellent individual characteristic information. Finally, the simulation testswere carried out on a standard example set. Compared with the traditional GA, the HBGA was improved regarding its global optimization ability, algorithmstability and running speed. In addition, the global optimization ability and stability of the HBGA were superior to the particle swarm optimization (PSO) algorithm, ant colony optimization (ACO) algorithm and tabu search (TS) algorithm, but its running speed was slightly slower than the TS algorithm. The improvement of traditional GA is reasonable, and the overall solution performance of HBGA is better than the PSO algorithm, ACO algorithm and TS algorithm. |
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