###
计算机系统应用英文版:2019,28(2):152-157
←前一篇   |   后一篇→
本文二维码信息
码上扫一扫!
改进AHP-GA算法的多目标配送路径优化
(大连东软信息学院 智能与电子工程学院, 大连 116023)
Multi-Objective Location Routing Optimization of Improved AHP-GA
(School of Intelligence and Electronic Engineering, Dalian Neusoft University of Information, Dalian 116023, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 1585次   下载 2034
Received:July 31, 2018    Revised:August 30, 2018
中文摘要: 为准确优化快递配送路径,建立了基于时间窗的快递配送路径优化的数学模型.提出改进AHP-GA算法对多目标配送车辆路径进行优化,利用中位数层次分析算法对多个子目标进行权重系数配比,避免了极端值的影响,从而将多目标优化问题转化为单目标优化问题.通过简单的自然数对车辆路径进行编码,避免了路径重复.考虑了客户对车辆到达时间窗要求,包括车辆在约定时间之前到达获得的机会成本、在约定时间之后到达的罚金成本.最后,本文以1个配送中心,20个服务客户为例,对构建的数学模型通过分别使用传统的GA算法和使用改进AHP-GA算法进行优化,仿真结果表明,利用改进AHP-GA算法进行多目标配送路径优化,可以更加高效地求得问题的最优解.
Abstract:In order to optimize the delivery path of express delivery, a mathematical model based on time window is given. In this study, improved AHP-GA algorithm is used to optimize multi-target vehicle routing, and median Analytic Hierarchy Process (AHP) is used to compare the weight coefficients of multiple sub-targets, and it is not susceptible to extremes. Thus, the multi-objective optimization problem is transformed into a single objective optimization problem. The simple natural numbers are used to code the vehicle path to avoid duplication of the paths. The customer's requirement for arrival time window, including the opportunity cost of the vehicle to arrive before the agreed time, and the cost of the fine after the agreed time. Finally, this study takes 1 distribution center and 20 service customers for example, the mathematical model constructed in this study is optimized by using traditional GA algorithm and using improved AHP-GA algorithm respectively. The simulation results show that the optimal solution can be obtained efficiently by using improved AHP-GA algorithm in multi-objective distribution path optimization problem.
文章编号:     中图分类号:    文献标志码:
基金项目:辽宁省自然科学基金(20170520398);辽宁省教育厅科学研究一般项目(L2015041,L2012492)
引用文本:
李凤坤.改进AHP-GA算法的多目标配送路径优化.计算机系统应用,2019,28(2):152-157
LI Feng-Kun.Multi-Objective Location Routing Optimization of Improved AHP-GA.COMPUTER SYSTEMS APPLICATIONS,2019,28(2):152-157