自动化立体仓库退库货位优化问题及其求解算法  

Retrieval-oriented storage relocation problem of an automated storage and retrieval system and its solving algorithm

作　　者：何在祥 李丽[1] 张云峰 郗琳 HE Zaixiang;LI Li;ZHANG Yunfeng;XI Lin(College of Engineering and Technology,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学工程技术学院,重庆400715

出　　处：《重庆理工大学学报（自然科学）》2024年第3期183-194,共12页Journal of Chongqing University of Technology：Natural Science

基　　金：重庆市杰出青年科学基金项目(CSTB2022NSCQ-JQX0030);中央高校基本科研业务费专项资金项目(SWU-XDJH202302)。

摘　　要：针对自动化立体仓库出库作业过程中剩余货物退库问题,以堆垛机作业总能耗最小化为目标,以退库货位分配为决策变量,建立了自动化立体仓库退库货位优化模型,提出了基于深度强化学习的自动化立体仓库退库货位优化框架。在该框架内,以立体仓库实时存储信息和出库作业信息构建多维状态,以退库货位选择构建动作,建立自动化立体仓库退库货位优化的马尔科夫决策过程模型;将立体仓库多维状态特征输入双层决斗网络,采用决斗双重深度Q网络(dueling double deep Q-network,D3QN)算法训练网络模型并预测退库动作目标价值,以确定智能体的最优行为策略。实验结果表明D3QN算法在求解大规模退库货位优化问题上具有较好的稳定性。The efficient retrieval handling of storage products in automated storage and retrieval system(AS/RS)is the key for just-in-time production in industrial production systems.In a production-oriented AS/RS,the amount of a specific product to be retrieved is often less than a pallet load.On completion of each retrieval request,the remaining products on the pallet should be relocated to a specific rack location.In current practice,after retrieving certain items,a non-empty pallet is typically returned to the originally designated location.It is of significant importance to dynamically re-assigning the storage locations of non-empty pallets after each retrieval in order to improve retrieval operational efficiency as well as energy consumption.In this study,a retrieval-oriented storage relocation problem is investigated to minimize energy consumption.Based on the characteristics of the acceleration and deceleration motion of the stacker crane and the crane’s dual operation mode,an energy consumption model of AS/RS during retrieval processes is first established.Then the relocation of non-empty pallet after each retrieval request is taken as an operational decision and the constraints including rack stability and maximum storage capacity of each rack location are considered,and an optimization model of retrieval-oriented storage relocation is built with the objective of minimizing the total energy consumption during the retrieval processes.In consideration of the NP-hard characteristic of the optimization model,this paper proposes an optimization framework based on deep reinforcement learning algorithm.Within this framework,the multi-dimensional state is designed based on the real-time storage information as well as the retrieval information and the action is constructed by the retrieval-oriented storage relocation.A Markov decision process model for retrieval-oriented storage relocation is then established and the dueling double deep Q-networks(D3QN)algorithm is employed to obtain the optimal storage relocation of no

关 键 词：自动化立体仓库 退库货位优化 深度强化学习 D3QN 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]