Operating policies for robotic cellular warehousing systems

Benedict Jun Ma, Shenle Pan (), Bipan Zou, Yong-Hong Kuo and George Huang
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Benedict Jun Ma: Department of Data and Systems Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
Shenle Pan: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique, Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres
Bipan Zou: School of Business Administration, Zhongnan University of Economics and Law, Wuhan, China
Yong-Hong Kuo: Department of Data and Systems Engineering, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China
George Huang: Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, China

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Abstract: Robotic Cellular Warehousing Systems provide an innovative robot-to-goods picking approach designed to improve robot transportation efficiency, where robots move to pick items and transport the picked items to workstations. In this study, we investigate the optimal operating policies for such a system by comparing two picking strategies (pick-while-sort and pick-then-sort) and three robot-to-workstation assignment rules (random, closest, and dedicated). Specifically, we develop dedicated closed queuing networks to model robot-to-goods picking and estimate warehouse throughput under different policies through single-class and multi-class models. The effectiveness of these analytical models is validated through numerical simulations, with an average gap of 5.53% between simulation and analytical results. Additionally, we conduct a series of numerical experiments to examine the impact of various factors on warehouse performance, including the numbers of robots and workstations, robot capacity, order size, and sorting efficiency. Based on the experimental findings, we provide managerial implications that offer insights into optimizing resource allocation and system configuration. These insights enable warehouse managers to improve operational efficiency and overall performance.

Date: 2025-02
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Published in Transportation Research Part E: Logistics and Transportation Review, 2025, 194, pp.103875. ⟨10.1016/j.tre.2024.103875⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04818009

DOI: 10.1016/j.tre.2024.103875

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