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Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS)

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  • Riccardo Manzini
  • Riccardo Accorsi
  • Giulia Baruffaldi
  • Teresa Cennerazzo
  • Mauro Gamberi

Abstract

Autonomous vehicle storage and retrieval systems use vehicles that move horizontally along rails within the storage racks, while vertical movements are provided by lifts. The solution proposed in this paper addresses a particular system configuration that works with multiple deep storage lanes that are widely used in the food and beverage industry, characterised by large volumes of products of limited variety. The generic deep lane is single item, i.e. one stock keeping unit, and single batch, i.e. one production lot, thereby affecting the performance of the system in terms of storage capacity utilisation and throughput. Determining the number and depth of the lanes is crucial to aid the design and control of such a storage system. The aim of this paper was to support the design of AVS/RSs though a set of original analytic models for the determination of the travelled distance and time for single-command and dual-command cycles given alternative layout configurations. The models are validated by simulation and exemplified with a real-warehousing case study. The paper presents useful guidelines for the configuration of the system layout including the determination of the optimal shape ratio and the length of the lanes.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:14:p:4286-4304
    DOI: 10.1080/00207543.2016.1144241
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    References listed on IDEAS

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    1. Fukunari, Miki & Malmborg, Charles J., 2009. "A network queuing approach for evaluation of performance measures in autonomous vehicle storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 193(1), pages 152-167, February.
    2. Li Zhang & Ananth Krishnamurthy & Charles J. Malmborg & Sunderesh S. Heragu, 2009. "Variance-based approximations of transaction waiting times in autonomous vehicle storage and retrieval systems," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(2), pages 146-169.
    3. 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.
    4. Roodbergen, Kees Jan & Vis, Iris F.A., 2009. "A survey of literature on automated storage and retrieval systems," European Journal of Operational Research, Elsevier, vol. 194(2), pages 343-362, April.
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    Cited by:

    1. Dong, Wenquan & Jin, Mingzhou, 2024. "Automated storage and retrieval system design with variant lane depths," European Journal of Operational Research, Elsevier, vol. 314(2), pages 630-646.
    2. Yang, Jingjing & de Koster, René B.M. & Guo, Xiaolong & Yu, Yugang, 2023. "Scheduling shuttles in deep-lane shuttle-based storage systems," European Journal of Operational Research, Elsevier, vol. 308(2), pages 696-708.
    3. Chen, Wanying (Amanda) & De Koster, René & Gong, Yeming, 2023. "Warehouses without aisles: Layout design of a multi-deep rack climbing robotic system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).
    4. 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.
    5. Emanuele Guerrazzi & Valeria Mininno & Davide Aloini & Riccardo Dulmin & Claudio Scarpelli & Marco Sabatini, 2019. "Energy Evaluation of Deep-Lane Autonomous Vehicle Storage and Retrieval System," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    6. Wu, Guangmei & Xu, Xianhao & Gong, Yeming (Yale) & De Koster, René & Zou, Bipan, 2019. "Optimal design and planning for compact automated parking systems," European Journal of Operational Research, Elsevier, vol. 273(3), pages 948-967.
    7. Yang, Peng & Yang, Kaidong & Qi, Mingyao & Miao, Lixin & Ye, Bin, 2017. "Designing the optimal multi-deep AS/RS storage rack under full turnover-based storage policy based on non-approximate speed model of S/R machine," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 104(C), pages 113-130.
    8. 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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