Journal of Computer Applications ›› 2020, Vol. 40 ›› Issue (1): 284-291.DOI: 10.11772/j.issn.1001-9081.2019061035

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Storage location assignment optimization of stereoscopic warehouse based on genetic simulated annealing algorithm

ZHU Jie, ZHANG Wenyi, XUE Fei   

  1. School of Information, Beijing Wuzi University, Beijing 100149, China
  • Received:2019-06-19 Revised:2019-08-06 Online:2020-01-10 Published:2019-09-29
  • Contact: 张文怡
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (71371033), the Science and Technology Program of Beijing Municipal Education Commission (KM201810037002), the Beijing Intelligent Logistics System Collaborative Innovation Center Project (0351701301).


朱杰, 张文怡, 薛菲   

  1. 北京物资学院 信息学院, 北京 100149
  • 作者简介:朱杰(1960-),男,北京人,教授,博士,主要研究方向:智能物联网工程;张文怡(1995-),女,北京人,硕士研究生,主要研究方向:智能物流系统;薛菲(1985-),男,山东滨州人,讲师,博士,主要研究方向:模式识别、机器学习。
  • 基金资助:

Abstract: Concerning the problem of storage location assignment in automated warehouse, combined with the operational characteristics and security requirements of warehouse, a multi-objective model for automated stereoscopic warehouse storage location assignment was constructed, and an adaptive improved Simulated Annealing Genetic Algorithm (SAGA) based on Sigmoid curve for solving the model was proposed. Firstly, aiming at reducing the loading and unloading time of items, the distance between items in same group and the gravity center of shelf, a storage location optimization model was established. Then, in order to overcome the shortcomings of poor local search ability and being easily fall into local optimum of Genetic Algorithm (GA), the adaptive cross mutation operation based on Sigmoid curve and the reversed operation were introduced and fused with SAGA. Finally, the optimization, stability and convergence of the improved genetic SAGA were tested. The experimental results show that compared with Simulated Annealing (SA) algorithm, the proposed algorithm has the optimization degree of loading and unloading time of items increased by 37.7949 percentage points, the optimization degree of distance between items in same group improved by 58.4630 percentage points, and optimization degree of gravity center of shelf increased by 25.9275 percentage points, meanwhile the algorithm has better stability and convergence. It proves the effectiveness of the improved genetic SAGA to solve the problem. The algorithm can provide a decision method for automated warehouse storage location assignment optimization.

Key words: automated stereoscopic warehouse, storage location assignment optimization, Genetic Algorithm (GA), Simulated Annealing (SA) algorithm, self-adaption

摘要: 针对自动化立体仓库储位分配问题,结合仓库运作特点和安全性要求,构建了自动化立体仓库储位优化问题的多目标模型,并提出了求解模型的基于Sigmoid曲线的改进自适应遗传模拟退火算法(SAGA)。首先,以降低货品出入库时间、同组货品距离和货架重心为目标建立储位优化模型;然后,为了克服遗传算法(GA)局部搜索能力差和易陷入局部最优的缺点,引入基于Sigmoid曲线的自适应交叉变异操作和逆转操作,同时完成与SAGA的融合;最后,对改进遗传SAGA进行算法优化性、稳定性和收敛性测试。仿真实验表明,相比模拟退火(SA)算法的求解结果,该算法对货品出入库时间的优化度提高了37.7949个百分点、对同组货品距离提高了58.4630个百分点、对货架重心优化度提高了25.9275个百分点,并且该算法具有更好的稳定性和收敛性。由此验证了改进遗传SAGA求解问题的有效性,该算法可为自动化立体仓库储位优化提供决策方法。

关键词: 自动化立体仓库, 储位分配优化, 遗传算法, 模拟退火算法, 自适应

CLC Number: