Seaborne trade is the lynchpin in almost every international supply chain, and about 90% of non-bulk cargo worldwide is transported by container. In this survey we give an overview of data-driven optimization problems in liner shipping. Research in liner shipping is motivated by a need for handling still more complex decision problems, based on big data sets and going across several organizational entities. Moreover, liner shipping optimization problems are pushing the limits of optimization methods, creating a new breeding ground for advanced modelling and solution methods. Starting from liner shipping network design, we consider the problem of container routing and speed optimization. Next, we consider empty container repositioning and stowage planning as well as disruption management. In addition, the problem of bunker purchasing is considered in depth. In each section we give a clear problem description, bring an overview of the existing literature, and go in depth with a specific model that somehow is essential for the problem. We conclude the survey by giving an introduction to the public benchmark instances LINER-LIB. Finally, we discuss future challenges and give directions for further research.
机构:
Hong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R China
Xia, Jun
Li, Kevin X.
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Chung Ang Univ, Dept Int Logist, Seoul 156756, South KoreaHong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R China
Li, Kevin X.
Ma, Hong
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Zhejiang Univ, Sch Management, Hangzhou 310058, Zhejiang, Peoples R ChinaHong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R China
Ma, Hong
Xu, Zhou
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Hong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R ChinaHong Kong Polytech Univ, Dept Logist & Maritime Studies, Hong Kong, Hong Kong, Peoples R China
机构:
Univ Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R ChinaUniv Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Wang, Sainan
Gao, Suixiang
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Univ Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R ChinaUniv Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Gao, Suixiang
Tan, Tunzi
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Univ Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R ChinaUniv Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Tan, Tunzi
Yang, Wenguo
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Univ Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China
Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R ChinaUniv Chinese Acad Sci, Sch Math Sci, 19A YuQuan Rd, Beijing 100049, Peoples R China