各种因素影响下的综合泊位分配和码头起重机分配与调度问题

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2024-10-04 DOI:10.1049/cim2.70001
Meng Yu, Xuetao Liu, Xiaojing Ji, Yucong Ren, Wenjing Guo
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引用次数: 0

摘要

泊位和码头起重机(QC)作为集装箱码头的重要资源和设备,在实际运营中面临着各种挑战,其运营效率反过来又影响着整个码头的绩效。作者研究了在各种因素影响下的综合泊位分配和 QC 分配与调度问题,包括船舶到达时间不确定性和潮汐两个主要因素,以及泊位偏差和起重机间干扰两个次要因素。为了解决这个问题,作者建立了一个多因素鲁棒调度模型。在此基础上,设计了一种基于合同网协议(CNP)的遗传算法(GA)和脑风暴优化方法,以优化泊位和 QC 调度方案。具体来说,作者使用遗传算法进行个体编码和群体初始化,使用头脑风暴算法进行聚类,并引入 CNP 进行个体更新。实验结果表明,所设计的算法能使调度计划适应复杂环境,并能提高码头的服务水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Integrated berth allocation and quay crane assignment and scheduling problem under the influence of various factors

As the important resources and equipment of container terminals, berths and quay cranes (QCs) face various challenges in actual operations and their operation efficiency in turn affects the performance of the whole terminal. The authors investigate an integrated berth allocation and QC assignment and scheduling problem under the influence of various factors, including the two main factors of vessel arrival time uncertainty and tide, and the two secondary factors of berth deviation and interference between cranes. To formulate the problem, the authors develop a multi-factor robust scheduling model. A Genetic Algorithm (GA) with Brain Storm Optimisation based on the Contract Net Protocol (CNP) is designed to optimise the berth and QC scheduling scheme. Specifically, the authors use the GA for individual coding and population initialisation, use the brainstorming algorithm for clustering, and introduce the CNP for individual updating. The experimental results show that the designed algorithm can adapt the scheduling plan to complex environments and can improve the service level of terminals.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
期刊最新文献
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