基于节省里程的有序批处理变量邻域搜索算法研究

Zeping Pei, Zhuan Wang, Yiwen Yang
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引用次数: 3

摘要

以全集装箱装载的拣货到零件分批拣货系统为基础,建立了考虑拣货设备和商品包装体积的订单分批拣货模型,模型的目标是最大限度地节省拣货里程。为了求解该模型,提出了一种有序批处理变量邻域搜索算法。结合某物流中心的数据,进行了仿真实验。结果表明,在4种不同的订单池情况下,VNS-Deco的性能优于FCFS、SBBM和S&U-Deco, VNS-Deco优化后的总采摘里程比FCFS、SBBM和S&U-Deco分别减少了13.2%、3.3%和1.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research of Order Batching Variable Neighborhood Search Algorithm based on Saving Mileage
Basing on a picker-to-parts batch picking system of full container load, an order batching model takes picking equipment and commodity packaging volume into consideration is constructed, the objective of the model is to maximize the saving mileage. To solve the model, an order batching variable neighborhood search algorithm is proposed. With the data from a specific logistic center, a simulation experiment has been carried out. The results show that, the performance of VNS-Deco is superior to FCFS, SBBM and S&U-Deco under 4 different order pool situations, the total picking mileage optimized by VNS-Deco is reduced by 13.2%, 3.3% and 1.6% compared with FCFS, SBBM and S&U-Deco.
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