IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i21p9146-d1503791.html
   My bibliography  Save this article

Dynamic Flexible Allocation of Slots in Container Line Transport

Author

Listed:
  • Tingsong Wang

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Jiawei Liu

    (School of Management, Shanghai University, Shanghai 200444, China)

  • Yadong Wang

    (School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094, China)

  • Yong Jin

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

  • Shuaian Wang

    (Faculty of Business, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong)

Abstract

Due to the imbalance between supply and demand, liner container transportation often faces the problem of low slot utilization, which will occur in the shipping process, such as dry container demand exceeding the available dry slots and reefer slots not being fully utilized. This makes it important and challenging to maintain a balance between the actual demand and the limited number of slots allocated for liner container transport. Therefore, this study proposes a flexible allocation method: expanding the types of containers that can be loaded in the same slot. This method is suitable for handling each dynamic arrival container booking request by shipping enterprises, making decisions to accept or reject, and flexibly allocating shipping slots. In order to maximize the total revenue generated by accepting container booking requests during the entire booking acceptance cycle, we establish a dynamic programming model for the flexible allocation of slots. For model solving, we use the Q-learning reinforcement learning algorithm. Compared with traditional heuristic algorithms, this algorithm can improve solving efficiency and facilitate decision-making at the operational level of shipping enterprises. In terms of model performance, examples of different scales are used for comparison and training; the results are compared with the model without flexible allocation, and it is proved that the model proposed in this paper can obtain higher returns than the model without flexible allocation. The results show that the model and Q-learning algorithm can help enterprises solve the problem of the flexible allocation of shipping slots, and thus, this research has practical significance.

Suggested Citation

  • Tingsong Wang & Jiawei Liu & Yadong Wang & Yong Jin & Shuaian Wang, 2024. "Dynamic Flexible Allocation of Slots in Container Line Transport," Sustainability, MDPI, vol. 16(21), pages 1-23, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9146-:d:1503791
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/21/9146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/21/9146/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Tingsong & Meng, Qiang & Tian, Xuecheng, 2024. "Dynamic container slot allocation for a liner shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    2. Paul Tae-Woo Lee & Oh Kyoung Kwon & Xiao Ruan, 2019. "Sustainability Challenges in Maritime Transport and Logistics Industry and Its Way Ahead," Sustainability, MDPI, vol. 11(5), pages 1-9, March.
    3. Oben Ceryan & Ozge Sahin & Izak Duenyas, 2013. "Dynamic Pricing of Substitutable Products in the Presence of Capacity Flexibility," Manufacturing & Service Operations Management, INFORMS, vol. 15(1), pages 86-101, April.
    4. Gang Du & Chuanwang Sun & Jinxian Weng, 2016. "Liner Shipping Fleet Deployment with Sustainable Collaborative Transportation," Sustainability, MDPI, vol. 8(2), pages 1-15, February.
    5. Jeffrey I. McGill & Garrett J. van Ryzin, 1999. "Revenue Management: Research Overview and Prospects," Transportation Science, INFORMS, vol. 33(2), pages 233-256, May.
    6. D. K. Ryoo & H. A. Thanopoulou, 1999. "Liner alliances in the globalization era: a strategic tool for Asian container carriers," Maritime Policy & Management, Taylor & Francis Journals, vol. 26(4), pages 349-367, October.
    7. Chargui, Kaoutar & Zouadi, Tarik & Sreedharan, V. Raja & El Fallahi, Abdellah & Reghioui, Mohamed, 2023. "A novel robust exact decomposition algorithm for berth and quay crane allocation and scheduling problem considering uncertainty and energy efficiency," Omega, Elsevier, vol. 118(C).
    8. Han, Guanghua & Pu, Xujin & He, Zhou & Liu, Cong, 2018. "Integrated planning and allocation: A stochastic dynamic programming approach in container transportation," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 264-274.
    9. Strauss, Arne K. & Klein, Robert & Steinhardt, Claudius, 2018. "A review of choice-based revenue management: Theory and methods," European Journal of Operational Research, Elsevier, vol. 271(2), pages 375-387.
    10. 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.
    11. Qiang Meng & Shuaian Wang & Henrik Andersson & Kristian Thun, 2014. "Containership Routing and Scheduling in Liner Shipping: Overview and Future Research Directions," Transportation Science, INFORMS, vol. 48(2), pages 265-280, May.
    12. Bell, Michael G.H. & Liu, Xin & Angeloudis, Panagiotis & Fonzone, Achille & Hosseinloo, Solmaz Haji, 2011. "A frequency-based maritime container assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1152-1161, September.
    13. 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.
    14. Klein, Robert & Koch, Sebastian & Steinhardt, Claudius & Strauss, Arne K., 2020. "A review of revenue management: Recent generalizations and advances in industry applications," European Journal of Operational Research, Elsevier, vol. 284(2), pages 397-412.
    15. Chiew, Esther & Daziano, Ricardo A. & Garrow, Laurie A., 2017. "Bayesian estimation of hazard models of airline passengers’ cancellation behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 154-167.
    16. 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.
    17. Liang, Jinpeng & Li, Liming & Zheng, Jianfeng & Tan, Zhijia, 2023. "Service-oriented container slot allocation policy under stochastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 176(C).
    18. Shin-Chan Ting * & Gwo-Hshiung Tzeng, 2004. "An optimal containership slot allocation for liner shipping revenue management," Maritime Policy & Management, Taylor & Francis Journals, vol. 31(3), pages 199-211, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Shuaian, 2015. "Optimal sequence of container ships in a string," European Journal of Operational Research, Elsevier, vol. 246(3), pages 850-857.
    2. Wang, Hua & Wang, Shuaian & Meng, Qiang, 2014. "Simultaneous optimization of schedule coordination and cargo allocation for liner container shipping networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 261-273.
    3. Wang, Shuaian & Wang, Hua & Meng, Qiang, 2015. "Itinerary provision and pricing in container liner shipping revenue management," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 135-146.
    4. Wang, Yadong & Meng, Qiang & Du, Yuquan, 2015. "Liner container seasonal shipping revenue management," Transportation Research Part B: Methodological, Elsevier, vol. 82(C), pages 141-161.
    5. Gu, Yuyun & Wang, Yadong & Wang, Tingsong, 2025. "An approximate dynamic programming approach to dynamic slot allocation of spot containers with random arrivals, cancellations, and no-shows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 193(C).
    6. Wang, Tingsong & Meng, Qiang & Tian, Xuecheng, 2024. "Dynamic container slot allocation for a liner shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    7. Wang, Tingsong & Meng, Qiang & Wang, Shuaian & Qu, Xiaobo, 2021. "A two-stage stochastic nonlinear integer-programming model for slot allocation of a liner container shipping service," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 143-160.
    8. Fukushi, Mitsuyoshi & Delgado, Felipe & Raveau, Sebastián & Santos, Bruno F., 2022. "CHAIRS: A choice-based air transport simulator applied to airline competition and revenue management," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 297-315.
    9. Liang, Jinpeng & Ma, Zhongyuan & Wang, Shuang & Liu, Haitao & Tan, Zhijia, 2024. "Dynamic container slot allocation with empty container repositioning under stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 187(C).
    10. Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Laporte, Gilbert, 2020. "Green technology adoption for fleet deployment in a shipping network," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 388-410.
    11. Wang, Shuaian & Liu, Zhiyuan & Meng, Qiang, 2015. "Segment-based alteration for container liner shipping network design," Transportation Research Part B: Methodological, Elsevier, vol. 72(C), pages 128-145.
    12. Wang, Kai & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2017. "Cruise service planning considering berth availability and decreasing marginal profit," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 1-18.
    13. Meng, Qiang & Du, Yuquan & Wang, Yadong, 2016. "Shipping log data based container ship fuel efficiency modeling," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 207-229.
    14. Meng, Qiang & Zhao, Hui & Wang, Yadong, 2019. "Revenue management for container liner shipping services: Critical review and future research directions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 128(C), pages 280-292.
    15. 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.
    16. Zhen, Lu & Hu, Yi & Wang, Shuaian & Laporte, Gilbert & Wu, Yiwei, 2019. "Fleet deployment and demand fulfillment for container shipping liners," Transportation Research Part B: Methodological, Elsevier, vol. 120(C), pages 15-32.
    17. Meng, Qiang & Lee, Chung-Yee, 2016. "Liner container assignment model with transit-time-sensitive container shipment demand and its applicationsAuthor-Name: Wang, Shuaian," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 135-155.
    18. Wang, Shuaian & Qu, Xiaobo & Yang, Ying, 2015. "Estimation of the perceived value of transit time for containerized cargoes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 298-308.
    19. Angeloudis, Panagiotis & Greco, Luciano & Bell, Michael G.H., 2016. "Strategic maritime container service design in oligopolistic markets," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 22-37.
    20. Sun, Zhuo & Zheng, Jianfeng, 2016. "Finding potential hub locations for liner shipping," Transportation Research Part B: Methodological, Elsevier, vol. 93(PB), pages 750-761.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9146-:d:1503791. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.