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Risk management in liner ship fleet deployment: A joint chance constrained programming model

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  • Wang, Tingsong
  • Meng, Qiang
  • Wang, Shuaian
  • Tan, Zhijia

Abstract

This paper provides a tangible methodology to deal with the liner ship fleet deployment problem aiming at minimizing the total cost while maintaining a service level under uncertain container demand. The problem is first formulated as a joint chance constrained programming model, and the sample average approximation method and mixed-integer programming are used to deal with it. Finally, a numerical example of a liner shipping network is carried out to verify the applicability of the proposed model and solution algorithm. It is found that the service level has significant effect on the total cost.

Suggested Citation

  • Wang, Tingsong & Meng, Qiang & Wang, Shuaian & Tan, Zhijia, 2013. "Risk management in liner ship fleet deployment: A joint chance constrained programming model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 60(C), pages 1-12.
    • Handle: RePEc:eee:transe:v:60:y:2013:i:c:p:1-12
    DOI: 10.1016/j.tre.2013.09.001
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    Cited by:

    1. Ng, ManWo, 2017. "Revisiting a class of liner fleet deployment models," European Journal of Operational Research, Elsevier, vol. 257(3), pages 773-776.
    2. Mihaela Bukljaš & Kristijan Rogić & Vladimir Jerebić, 2022. "Distributionally Robust Model and Metaheuristic Frame for Liner Ships Fleet Deployment," Sustainability, MDPI, vol. 14(9), pages 1-18, May.
    3. Wang, Tingsong & Tian, Xuecheng & Wang, Yadong, 2020. "Container slot allocation and dynamic pricing of time-sensitive cargoes considering port congestion and uncertain demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 144(C).
    4. Sun, X.T. & Chung, S.H. & Chan, Felix T.S. & Wang, Zheng, 2018. "The impact of liner shipping unreliability on the production–distribution scheduling of a decentralized manufacturing system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 242-269.
    5. Ksciuk, Jana & Kuhlemann, Stefan & Tierney, Kevin & Koberstein, Achim, 2023. "Uncertainty in maritime ship routing and scheduling: A Literature review," European Journal of Operational Research, Elsevier, vol. 308(2), pages 499-524.
    6. Yi Zhao & Ronghui Liu & Xi Zhang & Anthony Whiteing, 2018. "A chance-constrained stochastic approach to intermodal container routing problems," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-22, February.
    7. Wang, Xin & Fagerholt, Kjetil & Wallace, Stein W., 2018. "Planning for charters: A stochastic maritime fleet composition and deployment problem," Omega, Elsevier, vol. 79(C), pages 54-66.
    8. Demir, Emrah & Burgholzer, Wolfgang & Hrušovský, Martin & Arıkan, Emel & Jammernegg, Werner & Woensel, Tom Van, 2016. "A green intermodal service network design problem with travel time uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 789-807.
    9. Sun, Xuting & Chung, Sai-Ho & Choi, Tsan-Ming & Sheu, Jiuh-Biing & Ma, Hoi Lam, 2020. "Combating lead-time uncertainty in global supply chain's shipment-assignment: Is it wise to be risk-averse?," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 406-434.
    10. E. Zhang & Feng Chu & Shijin Wang & Ming Liu & Yang Sui, 2022. "Approximation approach for robust vessel fleet deployment problem with ambiguous demands," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2180-2194, November.
    11. Ng, ManWo, 2015. "Container vessel fleet deployment for liner shipping with stochastic dependencies in shipping demand," Transportation Research Part B: Methodological, Elsevier, vol. 74(C), pages 79-87.
    12. Wang, Shuaian, 2014. "A novel hybrid-link-based container routing model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 165-175.
    13. Wetzel, Daniel & Tierney, Kevin, 2020. "Integrating fleet deployment into liner shipping vessel repositioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    14. Ng, ManWo, 2014. "Distribution-free vessel deployment for liner shipping," European Journal of Operational Research, Elsevier, vol. 238(3), pages 858-862.
    15. E. Zhang & Feng Chu & Shijin Wang & Ming Liu & Yang Sui, 0. "Approximation approach for robust vessel fleet deployment problem with ambiguous demands," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-15.
    16. Fischer, Andreas & Nokhart, Håkon & Olsen, Henrik & Fagerholt, Kjetil & Rakke, Jørgen Glomvik & Stålhane, Magnus, 2016. "Robust planning and disruption management in roll-on roll-off liner shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 51-67.
    17. Lai, Xiaofan & Wu, Lingxiao & Wang, Kai & Wang, Fan, 2022. "Robust ship fleet deployment with shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 161(C), pages 169-196.

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