Efficient routing for multiple AGVs in container terminals using hybrid deep learning and metaheuristic algorithm

IF 6 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Ain Shams Engineering Journal Pub Date : 2025-05-12 DOI:10.1016/j.asej.2025.103468

Bui Minh Hau , Sam-Sang You , Le Ngoc Bao Long , Hwan-Seong Kim

{"title":"Efficient routing for multiple AGVs in container terminals using hybrid deep learning and metaheuristic algorithm","authors":"Bui Minh Hau , Sam-Sang You , Le Ngoc Bao Long , Hwan-Seong Kim","doi":"10.1016/j.asej.2025.103468","DOIUrl":null,"url":null,"abstract":"<div><div>This study aims to present the efficient routing of multiple agent systems by optimizing the automated guided vehicle (AGV) movement, waiting time, or container-lifting actions, providing optimal routing solutions in automated container terminals (ACTs). Through integrated scheduling, the AGV agent can determine the efficient route to the container, then pick it up and transport it to the final destination. Users can provide the starting position of the AGVs, container position, and drop-down location. The algorithm returns action lists for the AGV to perform. AGV route is implemented based on mapping the action in the action lists and the layout, preventing collisions and deadlocks among AGVs. We utilized the advantage actor-critic (A2C) reinforcement learning method combined with the ant colony optimization (ACO) of a swarm intelligence algorithm to solve the optimal routing problem in AGV-based ACTs. More specifically, this study presents the optimal action strategy that ACO-A2C finds for each AGV and a route scheme that each AGV can travel without colliding with other AGVs and obstacles. This novel method can potentially improve ACTs’ equipment utilization for efficient and competitive management.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 8","pages":"Article 103468"},"PeriodicalIF":6.0000,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447925002096","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

This study aims to present the efficient routing of multiple agent systems by optimizing the automated guided vehicle (AGV) movement, waiting time, or container-lifting actions, providing optimal routing solutions in automated container terminals (ACTs). Through integrated scheduling, the AGV agent can determine the efficient route to the container, then pick it up and transport it to the final destination. Users can provide the starting position of the AGVs, container position, and drop-down location. The algorithm returns action lists for the AGV to perform. AGV route is implemented based on mapping the action in the action lists and the layout, preventing collisions and deadlocks among AGVs. We utilized the advantage actor-critic (A2C) reinforcement learning method combined with the ant colony optimization (ACO) of a swarm intelligence algorithm to solve the optimal routing problem in AGV-based ACTs. More specifically, this study presents the optimal action strategy that ACO-A2C finds for each AGV and a route scheme that each AGV can travel without colliding with other AGVs and obstacles. This novel method can potentially improve ACTs’ equipment utilization for efficient and competitive management.

查看原文本刊更多论文

基于深度学习和元启发式混合算法的集装箱码头多agv高效路由

本研究旨在通过优化自动导引车（AGV）运动、等待时间或集装箱起重动作，呈现多智能体系统的高效路由，为自动化集装箱码头（ACTs）提供最优路由解决方案。AGV代理通过集成调度，确定到达集装箱的有效路线，然后取箱运输到最终目的地。用户可以提供agv起始位置、集装箱位置和下拉位置。该算法返回AGV要执行的动作列表。AGV路由是基于动作列表中的动作与布局的映射来实现的，防止了AGV之间的碰撞和死锁。利用优势行为者-批评家（A2C）强化学习方法结合群智能算法中的蚁群优化（ACO）来解决基于agv的ACTs中的最优路由问题。更具体地说，本研究给出了ACO-A2C为每个AGV找到的最优行动策略，以及每个AGV在不与其他AGV和障碍物碰撞的情况下行驶的路线方案。这种新方法可以潜在地提高ACTs的设备利用率，以实现高效和竞争性的管理。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Ain Shams Engineering Journal Engineering-General Engineering

CiteScore

10.80

自引率

13.30%

发文量

441

审稿时长

49 weeks

期刊介绍： in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.