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Multi-period liner ship fleet planning with dependent uncertain container shipment demand

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  • Qiang Meng
  • Tingsong Wang
  • Shuaian Wang

Abstract

This paper deals with a realistic multi-period liner ship fleet planning problem by incorporating stochastic dependency of the random and period-dependent container shipment demand. This problem is formulated as a multi-period stochastic programming model with a sequence of interrelated two-stage stochastic programming (2SSP) problems characterized ship fleet planning in each single period. A solution method integrating dual decomposition and Lagrangian relaxation method is designed for solving the developed model. Numerical experiments are carried out to assess applicability and performance of the proposed model and solution algorithm. The results further demonstrate importance of stochastic dependence of the uncertain container shipment demand.

Suggested Citation

  • Qiang Meng & Tingsong Wang & Shuaian Wang, 2015. "Multi-period liner ship fleet planning with dependent uncertain container shipment demand," Maritime Policy & Management, Taylor & Francis Journals, vol. 42(1), pages 43-67, January.
    • Handle: RePEc:taf:marpmg:v:42:y:2015:i:1:p:43-67
    DOI: 10.1080/03088839.2013.865848
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    References listed on IDEAS

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    Cited by:

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    2. Yang, Zhongzhen & Jiang, Zhenfeng & Notteboom, Theo & Haralambides, Hercules, 2019. "The impact of ship scrapping subsidies on fleet renewal decisions in dry bulk shipping," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 126(C), pages 177-189.
    3. Christian Va Karsten & Stefan Ropke & David Pisinger, 2018. "Simultaneous Optimization of Container Ship Sailing Speed and Container Routing with Transit Time Restrictions," Transportation Science, INFORMS, vol. 52(4), pages 769-787, August.
    4. Guericke, Stefan & Tierney, Kevin, 2015. "Liner shipping cargo allocation with service levels and speed optimization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 84(C), pages 40-60.
    5. 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.
    6. Arslan, Ayşe N. & Papageorgiou, Dimitri J., 2017. "Bulk ship fleet renewal and deployment under uncertainty: A multi-stage stochastic programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 69-96.
    7. 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.
    8. Zhen, Lu & Xia, Jun & Huang, Lin & Wu, Yiwei, 2020. "Bus tour-based routing and truck deployment for small-package shipping companies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    9. 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.
    10. Gang Du & Chuanwang Sun & Jinxian Weng, 2016. "Liner Shipping Fleet Deployment with Sustainable Collaborative Transportation," Sustainability, MDPI, vol. 8(2), pages 1-15, February.
    11. Fu, Hao & Lam, William H.K. & Shao, Hu & Kattan, Lina & Salari, Mostafa, 2022. "Optimization of multi-type traffic sensor locations for estimation of multi-period origin-destination demands with covariance effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 157(C).

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