Shaorui Zhou , Xiaorui Liu , Jihong Chen , Min Zhao , Fan Wang , Lingxiao Wu
{"title":"海上物流业需求不确定班轮联盟舱位空箱协同配置联合优化","authors":"Shaorui Zhou , Xiaorui Liu , Jihong Chen , Min Zhao , Fan Wang , Lingxiao Wu","doi":"10.1016/j.tre.2025.104437","DOIUrl":null,"url":null,"abstract":"<div><div>With the growth traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-allocation has drawn wide attention as a way of cooperation within liner alliances. However, due to the need for liner alliances to predict uncertain future demands to formulate empty container scheduling plans, empty container repositioning and co-allocation are essential but seldom considered. In response to this, this paper jointly optimizes the slot and empty container co-allocation issues by considering slot and empty container sharing. First, we proposed a two-stage stochastic programming model to minimize the total cost of the liner alliance. To solve the high-dimensional stochastic programming problem, we then proposed two new hybrid stochastic learning algorithms, which utilize the network structure while adaptively approximating the objective function by learning from historical data. Finally, we tested our algorithm in the real route data of the Ocean Alliance between South Asia and Southeast Asia. Computational results indicate that the proposed algorithms exhibit effective and efficient performance for joint optimization of slot and empty container co-allocation for liner alliances with uncertain demand, and some interesting managerial insights can be drawn from the rolling-horizon experiments.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"204 ","pages":"Article 104437"},"PeriodicalIF":8.8000,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Joint optimization of slot and empty container co-allocation for liner alliances with uncertain demand in maritime logistics industry\",\"authors\":\"Shaorui Zhou , Xiaorui Liu , Jihong Chen , Min Zhao , Fan Wang , Lingxiao Wu\",\"doi\":\"10.1016/j.tre.2025.104437\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>With the growth traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-allocation has drawn wide attention as a way of cooperation within liner alliances. However, due to the need for liner alliances to predict uncertain future demands to formulate empty container scheduling plans, empty container repositioning and co-allocation are essential but seldom considered. In response to this, this paper jointly optimizes the slot and empty container co-allocation issues by considering slot and empty container sharing. First, we proposed a two-stage stochastic programming model to minimize the total cost of the liner alliance. To solve the high-dimensional stochastic programming problem, we then proposed two new hybrid stochastic learning algorithms, which utilize the network structure while adaptively approximating the objective function by learning from historical data. Finally, we tested our algorithm in the real route data of the Ocean Alliance between South Asia and Southeast Asia. Computational results indicate that the proposed algorithms exhibit effective and efficient performance for joint optimization of slot and empty container co-allocation for liner alliances with uncertain demand, and some interesting managerial insights can be drawn from the rolling-horizon experiments.</div></div>\",\"PeriodicalId\":49418,\"journal\":{\"name\":\"Transportation Research Part E-Logistics and Transportation Review\",\"volume\":\"204 \",\"pages\":\"Article 104437\"},\"PeriodicalIF\":8.8000,\"publicationDate\":\"2025-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part E-Logistics and Transportation Review\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1366554525004788\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554525004788","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Joint optimization of slot and empty container co-allocation for liner alliances with uncertain demand in maritime logistics industry
With the growth traffic of containerized shipping worldwide, container liners have seen increasing cooperation. Slot co-allocation has drawn wide attention as a way of cooperation within liner alliances. However, due to the need for liner alliances to predict uncertain future demands to formulate empty container scheduling plans, empty container repositioning and co-allocation are essential but seldom considered. In response to this, this paper jointly optimizes the slot and empty container co-allocation issues by considering slot and empty container sharing. First, we proposed a two-stage stochastic programming model to minimize the total cost of the liner alliance. To solve the high-dimensional stochastic programming problem, we then proposed two new hybrid stochastic learning algorithms, which utilize the network structure while adaptively approximating the objective function by learning from historical data. Finally, we tested our algorithm in the real route data of the Ocean Alliance between South Asia and Southeast Asia. Computational results indicate that the proposed algorithms exhibit effective and efficient performance for joint optimization of slot and empty container co-allocation for liner alliances with uncertain demand, and some interesting managerial insights can be drawn from the rolling-horizon experiments.
期刊介绍:
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.