Dynamic container slot allocation for a liner shipping service

In this paper, the authors study a dynamic container slot allocation problem (DCSAP) for a liner container shipping company that aims to make an acceptance or rejection decision to each dynamically arriving container slot booking request. To capture the dynamic arrival feature and real-time acceptance/rejection decision of the booking request, the authors formulate the DCSAP as a dynamic programming (DP) model with the objective of maximizing the total revenues generated by accepted container booking requests over the entire booking horizon. Due to the well-known curse of dimensionality of solving a DP model, the authors develop a series of models to transform the intractable DP model into a solvable approximate linear programming (ALP) model. The authors further propose a spatiotemporal-heterogeneity-based (STH-based) decomposition preprocessor by identifying the spatiotemporal property of the DCSAP before solving the ALP model. Extensive numerical experiments are conducted to assess the applicability of the developed research methodology.

• Record URL:
  • https://doi.org/10.1016/j.trb.2023.102874
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0191261523001996
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/01912615
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Tingsong
  • 0000-0002-4701-9652
  • Meng, Qiang
  • 0000-0003-4507-8947
  • Tian, Xuecheng
  • 0000-0002-5524-0082
• Publication Date: 2024-1

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 102874
• Serial:
  • Transportation Research Part B: Methodological
  • Volume: 179
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0191-2615
  • Serial URL: http://www.sciencedirect.com/science/journal/01912615

Subject/Index Terms

• TRT Terms: Dynamic programming; Liner shipping; Machine learning; Optimization; Slot allocation; Supply chain management
• Subject Areas: Data and Information Technology; Economics; Finance; Freight Transportation; Marine Transportation;

Filing Info

• Accession Number: 01905820
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 26 2024 10:02AM