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Modelling and analysis for multi-deep compact robotic mobile fulfilment system

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  • Peng Yang
  • Guang Jin
  • Guofang Duan

Abstract

With high efficiency and good scalability, Robotic Mobile Fulfilment Systems (RMFS) are increasingly applied in various warehouses, especially the e-commerce warehouses with rigid order completion time. RMFS requires less workers and provide more punctual service for customers. The existing literature on RMFS is based on single-deep non-compact layout. As land supply is limited and expensive in urban area, it’s essential to consider compact storage in RMFS. This paper is the first to model and evaluate the multi-deep compact RMFS. We develop a semi-open queueing network (SOQN) model to characterise the multi-deep compact RMFS and solve it by Approximate Mean Value Analysis (AMVA). The obtained approximate analytic solutions of system throughput, robot utilisation, and queue length were verified and assessed through simulations. The numerical experiments investigated the effects of different configuration of the lane depth, number of picking aisles, arrangement of picking stations and the number of robots on performance. Our research can provide useful guidelines for warehouse planners and managers for designing and operating multi-deep compact RMFS.

Suggested Citation

  • Peng Yang & Guang Jin & Guofang Duan, 2022. "Modelling and analysis for multi-deep compact robotic mobile fulfilment system," International Journal of Production Research, Taylor & Francis Journals, vol. 60(15), pages 4727-4742, August.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:15:p:4727-4742
    DOI: 10.1080/00207543.2021.1936264
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    Cited by:

    1. Li, Kunpeng & Liu, Tengbo & Ram Kumar, P.N. & Han, Xuefang, 2024. "A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 185(C).
    2. Ding, Tianrong & Zhang, Yuankai & Wang, Zheng & Hu, Xiangpei, 2024. "Velocity-based rack storage location assignment for the unidirectional robotic mobile fulfillment system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).

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