[1]杨文强,张素君,郭昊.求解仓储作业优化问题的多物种协同进化算法[J].郑州大学学报(工学版),2020,41(06):33-39.[doi:10.13705/j.issn.1671-6833.2019.03.030]
　YANG Wenqiang,ZHANG Sujun,GUO Hao.Operation Optimization of Warehousing by Multispecies Co-evolution Algorithm[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):33-39.[doi:10.13705/j.issn.1671-6833.2019.03.030]
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求解仓储作业优化问题的多物种协同进化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年06期
页码:
33-39
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
Operation Optimization of Warehousing by Multispecies Co-evolution Algorithm
作者:
杨文强; 张素君; 郭昊
河南科技学院机电学院,河南新乡453003, 河南科技学院机电学院,河南新乡453003, 河南科技学院机电学院,河南新乡453003

Author(s):
YANG Wenqiang;  ZHANG Sujun;  GUO Hao
School of Mechanical and Electrical Engineering, Henan Institute of Science and Technology, Xinxiang 453003, China
关键词:
Keywords:
warehouse operation optimization;  genetic algorithm;  particle swarm optimization algorithm;  artificial fish swarm algorithm;  multispecies co-evolution
DOI:
10.13705/j.issn.1671-6833.2019.03.030
文献标志码:
A
摘要:
针对一类带过道仓储作业优化问题，为提高仓储作业效率，受生物进化论的启发，提出基于遗传、粒子群以及人工鱼群共同参与的多物种协同进化算法（Multispecies Co-evolution Algorithm, MSCA），即通过基于学习机制的多物种竞争共生捕食策略，使每个物种适应环境的能力都能得到增强；同时引入变异机制，使全部物种的种群多样性得到协同改善，从而在提高单个物种进化能力的同时，也提高了算法的全局寻优能力及求解效率，最后通过工业现场实例验证了算法的有效性.
Abstract:
To improve the efficiency of warehousing operation, inspired by biological evolutionism, a new method of multispecies co-evolution algorithm (MSCA) based on genetic algorithm, particle swarm optimization algorithm, and artificial fish swarm algorithm was proposed to solve warehousing operation optimization problem with cross aisles in this paper. It improved the ability of each species to adapt to one′s environment based on multispecies competition-predatory strategies by learning mechanism; and could improve the population diversity of all the species cooperatively using mutation mechanism. It enchanced evolutional capacity of single species, and further improved the global search ability and solving efficiency of MSCA. Finally, industrial example showed the effectiveness of the proposed algorithm.

参考文献/References:

[1] ROODBERGEN K J, De KOSTER R. Routing order pickers in a warehouse with a middle aisle[J]. European journal of operational research, 2001, 133(1):32-34.

[2] HENN S, Wwidth=11,height=11,dpi=110SCHER G. Tabu search heuristics for the order batching problem in manual order picking systems[J]. European journal of operational research, 2012, 222(3):484-494.
[3] TSAI C Y, LIOU J J H, HUANG T M. Using a multiple-GA method to solve the batch picking problem: considering travel distance and order due time[J]. International journal of production research, 2008, 46(22):6533-6555.
[4] BOTTANI E, CECCONI M, VIGNALI G, et al. Optimisation of storage allocation in order picking operations through a genetic algorithm[J]. International journal of logistics research and applications, 2012, 15(2):127-146.
[5] De SANTIS R, MONTANARI R, VIGNALI G, et al. An adapted ant colony optimization algorithm for the minimization of the travel distance of pickers in manual warehouses[J]. European journal of operational research, 2018, 267(1):120-137.
[6] THEYS C, BRwidth=11,height=11,dpi=110YSY O, DULLAERT W, et al. Using a TSP heuristic for routing order pickers in warehouses[J]. European journal of operational research, 2010, 200(3):755-763.
[7] ASCHEUER N, GRÖTSCHEL M, ABDEL-HAMID A A. Order picking in an automatic warehouse: solving online asymmetric TSPs[J]. Mathematical methods of operations research, 1999, 49(3):501-515.
[8] LU W R, MCFARLANE D, GIANNIKAS V, et al. An algorithm for dynamic order-picking in warehouse operations[J]. European journal of operational research, 2016, 248(1):107-122.
[9] BOYSEN N, BRISKORN D, EMDE S. Sequencing of picking orders in mobile rack warehouses[J]. European journal of operational research, 2017, 259(1):293-307.
[10] 蔡安江,应嘉奇,王坚,等. 分散式立体仓库堆垛机调度模型[J]. 计算机集成制造系统, 2016, 22(3):793-799.
[11] 赵金萍,熊君星,邹文强,等. 基于Flexsim的自动化立体仓库出入库仿真与优化[J]. 高技术通讯, 2017, 27(1):81-87.
[12] 蒋美仙,冯定忠,赵晏林,等. 基于改进Fishbone的物流仓库布局优化[J]. 系统工程理论与实践, 2013, 33(11):2920-2929.
[13] 吴迪,王诺,宋南奇,等. 边远群岛物流体系的选址-库存-路径优化[J].系统工程理论与实践,2016, 36(12):3175-3187.
[14] 杨朋,缪立新,秦磊. 多载具自动化存取系统作业调度优化[J]. 计算机集成制造系统, 2013, 19(7):1626-1632.
[15] 刘臣奇,李梅娟,陈雪波. 基于蚁群算法的拣选作业优化问题[J]. 系统工程理论与实践, 2009, 29(3):179-185.
[16] 樊一娜,梁伟,黄渝清,等. 基于IGA的配电系统运行损耗与可靠性优化[J]. 郑州大学学报(工学版),2019, 40(5):58-63.
[17] 龙志伟,肖松毅,王晖,等. 基于粒子群算法的水资源需求预测[J]. 郑州大学学报(工学版), 2019, 40(4):32-35,47.
[18] 谢榕,潘维,柴崎亮介. 基于人工鱼群算法的出租车智能调度[J]. 系统工程理论与实践, 2017, 37(11):2938-2947.
[19] 黎明,卢明,陈昊,等. 基于线性映射的多物种捕食元胞遗传算法[J]. 模式识别与人工智能, 2013, 26(10):959-967.

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更新日期/Last Update: 2021-02-10