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Empty Container Management in Cyclic Shipping Routes

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  • Dong-Ping Song

    (International Shipping & Logistics Group, Business School, University of Plymouth, Plymouth PL4 8AA, UK.)

  • Jing-Xin Dong

    (International Shipping & Logistics Group, Business School, University of Plymouth, Plymouth PL4 8AA, UK.)

Abstract

This paper considers the empty container management problem in a cyclic shipping route. The objective is to seek the optimal empty container repositioning policy in a dynamic and stochastic situation by minimising the expected total costs consisting of inventory holding costs, demand lost-sale costs, lifting-on and lifting-off charges, and container transportation costs. A three-phase threshold control policy is developed to reposition empty containers in cyclic routes. The threshold values are determined based on the information of average customer demands and their variability. A non-repositioning policy and three other heuristic policies are introduced for purposes of comparison. Simulation is used to evaluate the performance of empty repositioning policies. A range of numerical examples with different demand patterns; degrees of uncertainty, and fleet sizes demonstrate that the threshold policy significantly outperforms the heuristic policies. Maritime Economics & Logistics (2008) 10, 335–361. doi:10.1057/mel.2008.11

Suggested Citation

  • Dong-Ping Song & Jing-Xin Dong, 2008. "Empty Container Management in Cyclic Shipping Routes," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 10(4), pages 335-361, December.
    • Handle: RePEc:pal:marecl:v:10:y:2008:i:4:p:335-361
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    Citations

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    Cited by:

    1. Kuzmicz, Katarzyna Anna & Pesch, Erwin, 2019. "Approaches to empty container repositioning problems in the context of Eurasian intermodal transportation," Omega, Elsevier, vol. 85(C), pages 194-213.
    2. Jeong, Yoonjea & Saha, Subrata & Moon, Ilkyeong, 2020. "Optimal devanning time and detention charges for container supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    3. Dongping Song, 2021. "A Literature Review, Container Shipping Supply Chain: Planning Problems and Research Opportunities," Logistics, MDPI, vol. 5(2), pages 1-26, June.
    4. Lee, Chung-Yee & Song, Dong-Ping, 2017. "Ocean container transport in global supply chains: Overview and research opportunities," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 442-474.
    5. Alaa Abdelshafie & May Salah & Tomaž Kramberger & Dejan Dragan, 2022. "Repositioning and Optimal Re-Allocation of Empty Containers: A Review of Methods, Models, and Applications," Sustainability, MDPI, vol. 14(11), pages 1-23, May.
    6. Wang, Tingsong & Xing, Zheng & Hu, Hongtao & Qu, Xiaobo, 2019. "Overbooking and delivery-delay-allowed strategies for container slot allocation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 433-447.
    7. Long, Yin & Lee, Loo Hay & Chew, Ek Peng, 2012. "The sample average approximation method for empty container repositioning with uncertainties," European Journal of Operational Research, Elsevier, vol. 222(1), pages 65-75.
    8. Yi Zhao & Qingwan Xue & Xi Zhang, 2018. "Stochastic Empty Container Repositioning Problem with CO 2 Emission Considerations for an Intermodal Transportation System," Sustainability, MDPI, vol. 10(11), pages 1-24, November.
    9. Bell, Michael G.H. & Liu, Xin & Rioult, Jeremy & Angeloudis, Panagiotis, 2013. "A cost-based maritime container assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 58-70.
    10. Najafi, Mehdi & Zolfagharinia, Hossein, 2021. "Pricing and quality setting strategy in maritime transportation: Considering empty repositioning and demand uncertainty," International Journal of Production Economics, Elsevier, vol. 240(C).
    11. Zhou, Shaorui & Zhang, Hui & Shi, Ning & Xu, Zhou & Wang, Fan, 2020. "A new convergent hybrid learning algorithm for two-stage stochastic programs," European Journal of Operational Research, Elsevier, vol. 283(1), pages 33-46.

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