Vehicle path optimization with time window and simultaneous delivery and pickup under fuzzy demand

Lei Zhou, Chenyang Zhang, Fachao Li
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Abstract

In order to improve the vehicle loading rate in the logistics and distribution process, this paper studies the vehicle routing problem with time window and simultaneous delivery and pickup under fuzzy demand. Triangle fuzzy numbers are introduced to describe the uncertainty of customer demand and construct a multi-objective function model with the goal of minimum total cost. Genetic algorithm (GA) is used to solve this problem. Compared with the simple delivery scheme and simple pickup scheme, the results show that the designed model and algorithm optimize the vehicle path and significantly reduce the number of vehicles, improve the average vehicle load rate and reduce the enterprise cost.
模糊需求下具有时间窗口和同时取货的车辆路径优化
为了提高物流配送过程中车辆的装货率,研究了模糊需求下带时间窗口、同时取货的车辆路径问题。引入三角模糊数来描述客户需求的不确定性,建立了以总成本最小为目标的多目标函数模型。遗传算法(GA)用于解决这一问题。结果表明,与简单配送方案和简单取货方案相比,所设计的模型和算法优化了车辆路径,显著减少了车辆数量,提高了车辆平均载重率,降低了企业成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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