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Inventory Allocation in Robotic Mobile Fulfillment Systems

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  • Lamballais, T.
  • Roy, D.
  • de Koster, M.B.M.

Abstract

A Robotic Mobile Fulfillment System is a recently developed automated, parts-to- picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage area and the workstations and can continually reposition them during operations. This paper shows how to optimize three key decision variables: (1) the number of pods per product (2) the ratio of the number of pick stations to replenishment stations, and (3) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This paper contributes methodologically by introducing a new type of Semi-Open Queueing Networks (SOQN): cross-class matching multi- class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes.

Suggested Citation

  • Lamballais, T. & Roy, D. & de Koster, M.B.M., 2017. "Inventory Allocation in Robotic Mobile Fulfillment Systems," ERIM Report Series Research in Management ERS-2017-001-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.
    • Handle: RePEc:ems:eureri:95392
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    References listed on IDEAS

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    3. Debjit Roy & Ananth Krishnamurthy & Sunderesh S. Heragu & Charles J. Malmborg, 2016. "A simulation framework for studying blocking effects in warehouse systems with autonomous vehicles," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 10(1), pages 51-80.
    4. Debjit Roy & Ananth Krishnamurthy & Sunderesh Heragu & Charles Malmborg, 2012. "Performance analysis and design trade-offs in warehouses with autonomous vehicle technology," IISE Transactions, Taylor & Francis Journals, vol. 44(12), pages 1045-1060.
    5. Roy, Debjit & Krishnamurthy, Ananth & Heragu, Sunderesh & Malmborg, Charles, 2015. "Queuing models to analyze dwell-point and cross-aisle location in autonomous vehicle-based warehouse systems," European Journal of Operational Research, Elsevier, vol. 242(1), pages 72-87.
    6. 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.
    7. Lamballais, T. & Roy, D. & De Koster, M.B.M., 2017. "Estimating performance in a Robotic Mobile Fulfillment System," European Journal of Operational Research, Elsevier, vol. 256(3), pages 976-990.
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    Cited by:

    1. Boysen, Nils & de Koster, René & Weidinger, Felix, 2019. "Warehousing in the e-commerce era: A survey," European Journal of Operational Research, Elsevier, vol. 277(2), pages 396-411.
    2. Calzavara, Martina & Sgarbossa, Fabio & Persona, Alessandro, 2019. "Vertical Lift Modules for small items order picking: an economic evaluation," International Journal of Production Economics, Elsevier, vol. 210(C), pages 199-210.
    3. Merschformann, M. & Lamballais, T. & de Koster, M.B.M. & Suhl, L., 2019. "Decision rules for robotic mobile fulfillment systems," Operations Research Perspectives, Elsevier, vol. 6(C).
    4. Roy, Debjit & Nigam, Shobhit & de Koster, René & Adan, Ivo & Resing, Jacques, 2019. "Robot-storage zone assignment strategies in mobile fulfillment systems," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 122(C), pages 119-142.

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