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Container relocation problem with time windows for container departure

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  • Ku, Dusan
  • Arthanari, Tiru S.

Abstract

The blocks relocation problem is a classic combinatorial optimisation problem that occurs in daily operations for facilities that use block stacking systems. In the block stacking method, blocks can be stored on top of each other in order to utilise the limited surface of a storage area. When there is a predetermined pickup order among the blocks, this stacking method inevitably leads to the reshuffling moves for blocks stored above the target block and the minimisation of such unproductive reshuffling moves is of a primary concern to industry practitioners. A container terminal is a typical place where this problem arises, thus the problem being also referred to as the container relocation problem. In this study, we consider departure time windows for containers, which are usually revealed by the truck appointment system in port container terminals. We propose a stochastic dynamic programming model to calculate the minimum expected number of reshuffles for a stack of containers which all have departure time windows. The model is solved with a search-based algorithm in a tree search space, and an abstraction heuristic is proposed to improve the time performance. To overcome the computational limitation of exact methods, we develop a heuristic called the expected reshuffling index (ERI) and evaluate its performance.

Suggested Citation

  • Ku, Dusan & Arthanari, Tiru S., 2016. "Container relocation problem with time windows for container departure," European Journal of Operational Research, Elsevier, vol. 252(3), pages 1031-1039.
  • Handle: RePEc:eee:ejores:v:252:y:2016:i:3:p:1031-1039
    DOI: 10.1016/j.ejor.2016.01.055
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    References listed on IDEAS

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    1. Namboothiri, Rajeev & Erera, Alan L., 2008. "Planning local container drayage operations given a port access appointment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(2), pages 185-202, March.
    2. Jin, Bo & Zhu, Wenbin & Lim, Andrew, 2015. "Solving the container relocation problem by an improved greedy look-ahead heuristic," European Journal of Operational Research, Elsevier, vol. 240(3), pages 837-847.
    3. Petering, Matthew E.H. & Hussein, Mazen I., 2013. "A new mixed integer program and extended look-ahead heuristic algorithm for the block relocation problem," European Journal of Operational Research, Elsevier, vol. 231(1), pages 120-130.
    4. De Castilho, Bernardo & Daganzo, Carlos F., 1993. "Handling Strategies for Import Containers at Marine Terminals," University of California Transportation Center, Working Papers qt5gr4622f, University of California Transportation Center.
    5. Caserta, Marco & Schwarze, Silvia & Voß, Stefan, 2012. "A mathematical formulation and complexity considerations for the blocks relocation problem," European Journal of Operational Research, Elsevier, vol. 219(1), pages 96-104.
    6. Katta G. Murty & Yat-wah Wan & Jiyin Liu & Mitchell M. Tseng & Edmond Leung & Kam-Keung Lai & Herman W. C. Chiu, 2005. "Hongkong International Terminals Gains Elastic Capacity Using a Data-Intensive Decision-Support System," Interfaces, INFORMS, vol. 35(1), pages 61-75, February.
    7. Zhao, Wenjuan & Goodchild, Anne V., 2010. "The impact of truck arrival information on container terminal rehandling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 46(3), pages 327-343, May.
    8. Kim, Kap Hwan & Park, Young Man & Ryu, Kwang-Ryul, 2000. "Deriving decision rules to locate export containers in container yards," European Journal of Operational Research, Elsevier, vol. 124(1), pages 89-101, July.
    9. Zehendner, Elisabeth & Feillet, Dominique, 2014. "Benefits of a truck appointment system on the service quality of inland transport modes at a multimodal container terminal," European Journal of Operational Research, Elsevier, vol. 235(2), pages 461-469.
    10. de Castillo, Bernardo & Daganzo, Carlos F., 1993. "Handling strategies for import containers at marine terminals," Transportation Research Part B: Methodological, Elsevier, vol. 27(2), pages 151-166, April.
    11. Zhang, Canrong & Chen, Weiwei & Shi, Leyuan & Zheng, Li, 2010. "A note on deriving decision rules to locate export containers in container yards," European Journal of Operational Research, Elsevier, vol. 205(2), pages 483-485, September.
    12. Saurí, S. & Martín, E., 2011. "Space allocating strategies for improving import yard performance at marine terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1038-1057.
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    Cited by:

    1. Jin, Bo & Tanaka, Shunji, 2023. "An exact algorithm for the unrestricted container relocation problem with new lower bounds and dominance rules," European Journal of Operational Research, Elsevier, vol. 304(2), pages 494-514.
    2. Feng, Yuanjun & Song, Dong-Ping & Li, Dong & Zeng, Qingcheng, 2020. "The stochastic container relocation problem with flexible service policies," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 116-163.
    3. Galle, Virgile & Barnhart, Cynthia & Jaillet, Patrick, 2018. "A new binary formulation of the restricted Container Relocation Problem based on a binary encoding of configurations," European Journal of Operational Research, Elsevier, vol. 267(2), pages 467-477.
    4. Azab, Ahmed & Morita, Hiroshi, 2022. "Coordinating truck appointments with container relocations and retrievals in container terminals under partial appointments information," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    5. Maniezzo, Vittorio & Boschetti, Marco A. & Gutjahr, Walter J., 2021. "Stochastic premarshalling of block stacking warehouses," Omega, Elsevier, vol. 102(C).
    6. Azab, Ahmed & Morita, Hiroshi, 2022. "The block relocation problem with appointment scheduling," European Journal of Operational Research, Elsevier, vol. 297(2), pages 680-694.
    7. Feng, Yuanjun & Song, Dong-Ping & Li, Dong, 2022. "Smart stacking for import containers using customer information at automated container terminals," European Journal of Operational Research, Elsevier, vol. 301(2), pages 502-522.
    8. Boschma, René & Mes, Martijn R.K. & de Vries, Leon R., 2023. "Approximate dynamic programming for container stacking," European Journal of Operational Research, Elsevier, vol. 310(1), pages 328-342.
    9. Zweers, Bernard G. & Bhulai, Sandjai & van der Mei, Rob D., 2020. "Optimizing pre-processing and relocation moves in the Stochastic Container Relocation Problem," European Journal of Operational Research, Elsevier, vol. 283(3), pages 954-971.
    10. Zhou, Chenhao & Wang, Wencheng & Li, Haobin, 2020. "Container reshuffling considered space allocation problem in container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 136(C).
    11. Filom, Siyavash & Amiri, Amir M. & Razavi, Saiedeh, 2022. "Applications of machine learning methods in port operations – A systematic literature review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    12. Huiling Zhu, 2022. "Integrated Containership Stowage Planning: A Methodology for Coordinating Containership Stowage Plan and Terminal Yard Operations," Sustainability, MDPI, vol. 14(20), pages 1-18, October.
    13. Kap Hwan Kim & Sanghyuk Yi, 2021. "Utilizing information sources to reduce relocation of inbound containers," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 23(4), pages 726-749, December.
    14. Gharehgozli, Amir & Zaerpour, Nima, 2018. "Stacking outbound barge containers in an automated deep-sea terminal," European Journal of Operational Research, Elsevier, vol. 267(3), pages 977-995.
    15. V. Galle & V. H. Manshadi & S. Borjian Boroujeni & C. Barnhart & P. Jaillet, 2018. "The Stochastic Container Relocation Problem," Transportation Science, INFORMS, vol. 52(5), pages 1035-1058, October.
    16. Caldeira dos Santos, Murillo & Pereira, Fábio Henrique, 2021. "Development and application of a dynamic model for road port access and its impacts on port-city relationship indicators," Journal of Transport Geography, Elsevier, vol. 96(C).
    17. Jiahao Zhao & Xiaoning Zhu & Li Wang, 2020. "Study on Scheme of Outbound Railway Container Organization in Rail-Water Intermodal Transportation," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    18. Lange, Ann-Kathrin & Nellen, Nicole & Jahn, Carlos, 2022. "Truck appointment systems: How can they be improved and what are their limits?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Jahn, Carlos & Blecker, Thorsten & Ringle, Christian M. (ed.), Changing Tides: The New Role of Resilience and Sustainability in Logistics and Supply Chain Management – Innovative Approaches for the Shift to a New , volume 33, pages 615-655, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    19. Raka Jovanovic & Shunji Tanaka & Tatsushi Nishi & Stefan Voß, 2019. "A GRASP approach for solving the Blocks Relocation Problem with Stowage Plan," Flexible Services and Manufacturing Journal, Springer, vol. 31(3), pages 702-729, September.
    20. Jovanovic, Raka & Tuba, Milan & Voß, Stefan, 2019. "An efficient ant colony optimization algorithm for the blocks relocation problem," European Journal of Operational Research, Elsevier, vol. 274(1), pages 78-90.
    21. Feng, Yuanjun & Song, Dong-Ping & Li, Dong & Xie, Ying, 2022. "Service fairness and value of customer information for the stochastic container relocation problem under flexible service policy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 167(C).
    22. Tanaka, Shunji & Tierney, Kevin & Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Ruiz, Rubén, 2019. "A branch and bound approach for large pre-marshalling problems," European Journal of Operational Research, Elsevier, vol. 278(1), pages 211-225.
    23. Raeesi, Ramin & Sahebjamnia, Navid & Mansouri, S. Afshin, 2023. "The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions," European Journal of Operational Research, Elsevier, vol. 310(3), pages 943-973.
    24. Silva, Marcos de Melo da & Erdoğan, Güneş & Battarra, Maria & Strusevich, Vitaly, 2018. "The Block Retrieval Problem," European Journal of Operational Research, Elsevier, vol. 265(3), pages 931-950.
    25. Facchini, F. & Digiesi, S. & Mossa, G., 2020. "Optimal dry port configuration for container terminals: A non-linear model for sustainable decision making," International Journal of Production Economics, Elsevier, vol. 219(C), pages 164-178.

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