首页> 外文会议>Service Systems and Service Management, 2009. ICSSSM '09 >Study on hybrid genetic algorithm for multi-type vehicles vehicle routing problem with backhauls
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Study on hybrid genetic algorithm for multi-type vehicles vehicle routing problem with backhauls

机译:带回程的多类型车辆路径问题的混合遗传算法研究

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In order to satisfy with the individual and various demand of customer, establish multi-type vehicles vehicle scheduling with picking-delivery model. According to the characteristics of model, hybrid genetic algorithm is used to get the optimization solution. First of all, use natural number coding so as to simplify the problem; use the individual amount control choice strategy so as to guarantee the diversity of group. Improved ordinal crossover operators can avoid destroying good gene parts during the course of ordinal crossover so as that the algorithm can be convergent to the optimization as whole. The study adopts 2-exchange mutation operator combine hill-climbing algorithm to strengthen the partial searching ability of chromosome. Secondly, stock elite adopting genetic algorithm take the hybrid genetic algorithm with taboo searching algorithm to improve the convergent speed and searching efficiency of algorithm. The emulation and calculation proves that it is better than only using genetic algorithm and taboo searching algorithm.
机译:为了满足客户的个性化和多样化需求,建立了具有提货-交付模型的多类型车辆调度。根据模型的特点,采用混合遗传算法得到优化解。首先,使用自然数编码以简化问题;使用个人的数量控制选择策略,以保证组的多样性。改进的有序交叉算子可以避免在有序交叉的过程中破坏良好的基因部分,从而使算法可以收敛到整个优化过程。本研究采用2-交换突变算子结合爬山算法来增强染色体的部分搜索能力。其次,采用遗传算法的股票精英将混合遗传算法与禁忌搜索算法结合使用,提高了算法的收敛速度和搜索效率。仿真和计算表明,该算法优于仅使用遗传算法和禁忌搜索算法。

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