{"title":"Real-time policy for yard allocation of transshipment containers in a terminal","authors":"Abdo Abouelrous, Rommert Dekker, Laurens Bliek, Yingqian Zhang","doi":"10.1016/j.trb.2024.103138","DOIUrl":null,"url":null,"abstract":"In this article, we investigate the problem of allocating storage space in a container terminal’s yard to transshipment containers. The main decision here concerns the block to which a container is assigned for storage until it is loaded later by another vessel. We propose a setting where some target performance measures are imposed on the discharge operations. In turn, the allocation decisions are made so as to reduce driving time from the storage blocks to the berth locations of the vessels that will pick up the containers. The trick here is to find an appropriate trade-off between the times spent on discharge and loading so that neither are delayed significantly. Using results from renewal theory, queuing theory and machine learning, we are able to quantify the effect of our allocation decisions on quay crane productivity. Thereafter, we formulate a mathematical optimization problem for the yard-allocation of containers and apply a meta-heuristic to solve it. Our method was developed for deployment by a software consultancy company for container terminals. We test our method using a real-time simulation and compare it with a benchmark from the literature. We show that our method generates reductions in vessel berth times and provide an overview on its economic impact.","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"5 1","pages":""},"PeriodicalIF":5.8000,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part B-Methodological","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.trb.2024.103138","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, we investigate the problem of allocating storage space in a container terminal’s yard to transshipment containers. The main decision here concerns the block to which a container is assigned for storage until it is loaded later by another vessel. We propose a setting where some target performance measures are imposed on the discharge operations. In turn, the allocation decisions are made so as to reduce driving time from the storage blocks to the berth locations of the vessels that will pick up the containers. The trick here is to find an appropriate trade-off between the times spent on discharge and loading so that neither are delayed significantly. Using results from renewal theory, queuing theory and machine learning, we are able to quantify the effect of our allocation decisions on quay crane productivity. Thereafter, we formulate a mathematical optimization problem for the yard-allocation of containers and apply a meta-heuristic to solve it. Our method was developed for deployment by a software consultancy company for container terminals. We test our method using a real-time simulation and compare it with a benchmark from the literature. We show that our method generates reductions in vessel berth times and provide an overview on its economic impact.
期刊介绍:
Transportation Research: Part B publishes papers on all methodological aspects of the subject, particularly those that require mathematical analysis. The general theme of the journal is the development and solution of problems that are adequately motivated to deal with important aspects of the design and/or analysis of transportation systems. Areas covered include: traffic flow; design and analysis of transportation networks; control and scheduling; optimization; queuing theory; logistics; supply chains; development and application of statistical, econometric and mathematical models to address transportation problems; cost models; pricing and/or investment; traveler or shipper behavior; cost-benefit methodologies.