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Modelling load retrievals in puzzle-based storage systems

Author

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  • Masoud Mirzaei
  • René B.M. De Koster
  • Nima Zaerpour

Abstract

Puzzle-based storage systems are a new type of automated storage systems that allow storage of unit loads (e.g. cars, pallets, boxes) in a rack on a very small footprint with individual accessibility of all loads. They resemble the famous 15-sliding tile puzzle. Current models for such systems study retrieving loads one at a time. However, much time can be saved by considering multiple retrieval loads simultaneously. We develop an optimal method to do this for two loads and heuristics for three or more loads. Optimal retrieval paths are constructed for multiple load retrieval, which consists of moving multiple loads first to an intermediary ‘joining location’. We find that, compared to individual retrieval, optimal dual load retrieval saves on average 17% move time, and savings from the heuristic is almost the same. For three loads, savings are 23% on average. A limitation of our method is that it is valid only for systems with a very high space utilisation, i.e. only one empty location is available. Future research should investigate retrieving multiple loads for systems with multiple empty slots.

Suggested Citation

  • Masoud Mirzaei & René B.M. De Koster & Nima Zaerpour, 2017. "Modelling load retrievals in puzzle-based storage systems," International Journal of Production Research, Taylor & Francis Journals, vol. 55(21), pages 6423-6435, November.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:21:p:6423-6435
    DOI: 10.1080/00207543.2017.1304660
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    Cited by:

    1. Azadeh, K. & de Koster, M.B.M. & Roy, D., 2017. "Robotized Warehouse Systems: Developments and Research Opportunities," ERIM Report Series Research in Management ERS-2017-009-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. He, Jing & Liu, Xinglu & Duan, Qiyao & Chan, Wai Kin (Victor) & Qi, Mingyao, 2023. "Reinforcement learning for multi-item retrieval in the puzzle-based storage system," European Journal of Operational Research, Elsevier, vol. 305(2), pages 820-837.
    3. MA, Yunfeng & CHEN, Haoxun & YU, Yugang, 2022. "An efficient heuristic for minimizing the number of moves for the retrieval of a single item in a puzzle-based storage system with multiple escorts," European Journal of Operational Research, Elsevier, vol. 301(1), pages 51-66.
    4. Fragapane, Giuseppe & de Koster, René & Sgarbossa, Fabio & Strandhagen, Jan Ola, 2021. "Planning and control of autonomous mobile robots for intralogistics: Literature review and research agenda," European Journal of Operational Research, Elsevier, vol. 294(2), pages 405-426.
    5. Bukchin, Yossi & Raviv, Tal, 2022. "A comprehensive toolbox for load retrieval in puzzle-based storage systems with simultaneous movements," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 348-373.
    6. Jianglong Yang & Li Zhou & Huwei Liu, 2021. "Hybrid genetic algorithm-based optimisation of the batch order picking in a dense mobile rack warehouse," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-25, April.
    7. Kaveh Azadeh & René De Koster & Debjit Roy, 2019. "Robotized and Automated Warehouse Systems: Review and Recent Developments," Transportation Science, INFORMS, vol. 53(4), pages 917-945, July.

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