基于改进克隆选择算法的仓储布局优化设计  

Optimization of Warehouse Layout Based on Improved Clonal Selection Algorithm

作　　者：曹霞 曹民[1] CAO Xia;CAO Min(School of Optical-electrical&Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《控制工程》2020年第2期329-334,共6页Control Engineering of China

基　　金：上海科技成果转化促进会、上海市教育发展基金会“联盟计划”项目(LM201763)。

摘　　要：为克服现有仓储布局优化策略存在的诸多缺陷,提出一种基于引入疫苗策略的改进克隆选择算法,通过在疫苗选取和接种过程中采用轮盘赌算法、克隆增殖过程中结合抗体亲和度和抗体浓度计算克隆规模、克隆抑制保留优良抗体、引入随机抗体等优化策略,提高了运算的收敛速度和寻优效率,增加了种群多样性,该算法实现满足存取作业需求和给定条件约束的仓储布局。仿真试验及对比分析表明,该算法收敛速度更快、存取路径更短,因而仓储效率更高。In order to overcome the defects existing in the optimization strategy of warehouse layout problem, this paper proposes an improved clonal selection algorithm based on the introduction of vaccine strategy. This algorithm adopts roulette algorithm in the process of vaccine selection and vaccination. Cloning scale, combining antibody affinity and antibody concentration, is calculated in the process of cloning and proliferation. The algorithm also retains excellent antibodies during cloning inhibition and introduces random antibodies. These optimization strategies which improve the convergence speed and optimization efficiency of the algorithm and increase the population diversity are used to realize the storage layout which satisfies the demand of the access job and the given condition constraint. Simulation analyses and comparisons show that this algorithm has the characteristics of faster convergence speed and shorter access path, thus greatly improving storage efficiency.

关 键 词：克隆选择 疫苗接种 仓储布局 优化设计 仿真试验 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TP253.4[自动化与计算机技术—计算机科学与技术]