基于改进免疫算法的容量限制工厂选址仿真  

A New Immune Genetic Algorithm for Capacitated Plant Location Problem

作　　者：李睿雪 马良[1] 刘勇[1] LI Rui-xue;MA Liang;LIU Yong(Management School,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《计算机仿真》2021年第12期401-405,共5页Computer Simulation

基　　金：教育部人文社会科学研究规划基金资助项目(16YJA630037);上海市“科技创新行动计划”软科学研究重点项目(18692110500);上海市社科规划课题(2019BGL014);上海市高原科学建设项目(第二期)。

摘　　要：针对传统容量限制的工厂选址问题存在的缺少对工厂生产能力和需求点需求量实际考虑等问题,建立根据需求选择工厂生产规模的选址模型,采用免疫遗传算法进行优化。通过对工厂的生产能力和需求进行分析,得到建设成本和运输费用,以此构建选址优化模型。对免疫遗传算法中记忆库容量改进为对个体评价动态设置记忆库容量以及采取Metropolis准则以改进遗传选择机制,避免陷入局部最优解。最后通过实验表明改进的模型和算法有效减少选址成本,提高工厂的利用率以及更好的满足需求。In the capacity-constrained plant location problem, it is found that there is a lack of practical considerations for the plant’s production capacity and demand point demand. Aiming at the above-mentioned problems, this paper established a new model which selected the production scale of the factory according to demand, and the immune genetic algorithm was used for optimization. Firstly, in order to build a location optimization model, total construction coat and transportation cost were acquired via analyzing the production capacity and demand of the factory.Then, memory capacity in the immune genetic algorithm was improved to dynamically set memory capacity through individual evaluation and metropolis criterion was adopted to improve the genetic selection mechanism to avoid falling into the local optimal solution. Finally, the simulation results show that the improved model and algorithm can effectively reduce the cost, improve the utilization rate of the factory and better satisfy demand.

关 键 词：容量限制 工厂选址 生产能力 需求点需求量 免疫遗传算法 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]