Page 149 - 《软件学报》2020年第9期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
Journal of Software,2020,31(9):2770−2784 [doi: 10.13328/j.cnki.jos.005944] http://www.jos.org.cn
©中国科学院软件研究所版权所有. Tel: +86-10-62562563

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智能仓储货位规划与 AGV 路径规划协同优化算法

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蔺一帅 , 李青山 , 陆鹏浩 , 孙雨楠 , 王 亮 , 王颖芝 1
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(西安电子科技大学 计算机科学与技术学院,陕西 西安 710071)
2 (苏州明逸智库信息技术有限公司,江苏 昆山 215300)
通讯作者: 李青山, E-mail: qshli@mail.xidian.edu.cn

摘 要: 智能仓储的优化一般分为货架优化和路径优化两部分:货架优化针对货物与货架两者的关系,对货物摆
放位置进行优化;而路径优化主要寻找自动引导小车(automated guided vehicle,简称 AGV)的最优路径.目前,大多的
智能仓储优化仅对这两部分进行独立研究.在实际仓储应用中,只能以线性叠加的方式解决问题,导致问题的求解易
陷入局部最优中.通过对智能仓储环节中各部分的关系进行耦合分析,提出了货位和 AGV 路径协同优化数学模型,
将货架优化和路径规划归为一个整体;此外,提出了智能仓储协同优化框架的求解算法,包括货品相似度求解算法和
改进的路径规划算法;并在以上两种算法的基础上,使用改进的遗传算法实现了货位路径协同优化.实验结果验证了
所提出的智能仓储协同优化算法的有效性和稳定性.通过使用该算法,可有效提高仓储的出货效率,降低运输成本.
关键词: 智能仓储;货位规划;AGV 路径规划;协同优化;遗传算法
中图法分类号: TP18

中文引用格式: 蔺一帅,李青山,陆鹏浩,孙雨楠,王亮,王颖芝.智能仓储货位规划与 AGV 路径规划协同优化算法.软件学报,
2020,31(9):2770−2784. http://www.jos.org.cn/1000-9825/5944.htm
英文引用格式: Lin YS, Li QS, Lu PH, Sun YN, Wang L, Wang YZ. Shelf and AGV path cooperative optimization algorithm
used in intelligent warehousing. Ruan Jian Xue Bao/Journal of Software, 2020,31(9):2770−2784 (in Chinese). http://www.jos.org.
cn/1000-9825/5944.htm

Shelf and AGV Path Cooperative Optimization Algorithm Used in Intelligent Warehousing

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LIN Yi-Shuai , LI Qing-Shan , LU Peng-Hao , SUN Yu-Nan , WANG Liang , WANG Ying-Zhi 1
1 (School of Computer Science and Technology, Xidian University, Xi’an 710071, China)
2 (Suzhou Mingyi Intelligent Storage Information Company, Kunshan 215300, China)
Abstract: The optimization of intelligent warehousing is generally divided into shelf optimization and path optimization. Shelf
optimization considers the position of goods and shelves, and optimizes the placement of goods. Path optimization mainly seeks the
optimal path planning for automatic guided vehicles. At present, most of the studies focus on these two scenarios independently. In the
actual warehousing application, the problem can only be solved by linear superposition, which makes the solution easy to fall into the
local optimum. Based on the coupling analysis of the relationship between various sections in the intelligent warehousing process, this
study proposes a mathematical model of cooperative optimization of shelf and position, which combines shelf optimization and path
planning as a whole. In addition, a cooperative optimization framework, including a product similarity solving algorithm and an improved
path planning algorithm, is proposed. Based on the above two algorithms, an improved genetic algorithm is proposed for the cooperative
optimization of shelf and path. The experimental results verify the effectiveness and stability of the intelligent warehousing cooperative

∗ 基金项目: 国家自然科学基金(61672401, 61902039, 61902288); 西安市科技计划(2017073CG/RC036(XDKD004))
Foundation item: National Natural Science Foundation of China (61672401, 61902039, 61902288); Xi’an Science and Technology
Program (2017073CG/RC036(XDKD004))
本文由“智能嵌入式系统”专题特约编辑王泉教授、吴中海教授、陈仪香教授、苗启广教授推荐.
收稿时间: 2019-07-02; 修改时间: 2019-08-18; 采用时间: 2019-11-02; jos 在线出版时间: 2020-01-13
CNKI 网络优先出版: 2020-01-14 11:27:19, http://kns.cnki.net/kcms/detail/11.2560.TP.20200114.1126.026.html