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The optimization of mixed block stacking requiring relocations

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  • Jang, Dong-Won
  • Kim, Se Won
  • Kim, Kap Hwan

Abstract

This paper addresses the optimization of a block stacking storage system (BSSS) in which unit loads are stored vertically. One of the important problems in a BSSS is in the relocation required when unit loads are located on top of the next unit load to be picked. Relocations are bound to occur when multiple types of unit loads are mixed in the same stacking area. Relocation is a major source of inefficiency during a BSSS handling operation. This study shows how the number of relocations can be reduced by utilizing the information regarding the arriving unit load type when determining its storage location. For the case where the information is not available, statistical models have been developed that estimate the expected number of relocations. For the case where the information is available and utilized, a method based on a genetic algorithm is suggested for use in determining the storage location for each arriving unit load in such a way that minimizes the expected number of relocations. A discussion is presented regarding how to determine the optimal number of stacks allocated to a set of unit load types which will share the same storage area considering the expected number of relocations.

Suggested Citation

  • Jang, Dong-Won & Kim, Se Won & Kim, Kap Hwan, 2013. "The optimization of mixed block stacking requiring relocations," International Journal of Production Economics, Elsevier, vol. 143(2), pages 256-262.
    • Handle: RePEc:eee:proeco:v:143:y:2013:i:2:p:256-262
    DOI: 10.1016/j.ijpe.2012.06.033
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Juan P. Cavada & Cristián E. Cortés & Pablo A. Rey, 2023. "Comparing allocation and relocation policies at a logistics service container terminal: a discrete-event simulation approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1281-1316, December.
    3. 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.
    4. Ji, Mingjun & Guo, Wenwen & Zhu, Huiling & Yang, Yongzhi, 2015. "Optimization of loading sequence and rehandling strategy for multi-quay crane operations in container terminals," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 80(C), pages 1-19.
    5. 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.
    6. Riccardo Manzini & Riccardo Accorsi & Giulia Baruffaldi & Teresa Cennerazzo & Mauro Gamberi, 2016. "Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS)," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4286-4304, July.
    7. 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).
    8. Zhang, Canrong & Wu, Tao & Kim, Kap Hwan & Miao, Lixin, 2014. "Conservative allocation models for outbound containers in container terminals," European Journal of Operational Research, Elsevier, vol. 238(1), pages 155-165.
    9. Zehendner, Elisabeth & Feillet, Dominique & Jaillet, Patrick, 2017. "An algorithm with performance guarantee for the Online Container Relocation Problem," European Journal of Operational Research, Elsevier, vol. 259(1), pages 48-62.
    10. Shahab Derhami & Jeffrey S. Smith & Kevin R. Gue, 2017. "Optimising space utilisation in block stacking warehouses," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6436-6452, November.
    11. Huiling Zhu & Mingjun Ji & Wenwen Guo & Qingbin Wang & Yongzhi Yang, 2019. "Mathematical formulation and heuristic algorithm for the block relocation and loading problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(4), pages 333-351, June.

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