Evaluating battery charging and swapping strategies in a robotic mobile fulfillment system

Bipan Zou, Xianhao Xu, Yeming Gong () and René de Koster
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Bipan Zou: Zhongnan University of Economics and Law [China]
Xianhao Xu: HUST - Huazhong University of Science and Technology [Wuhan]
Yeming Gong: EM - EMLyon Business School
René de Koster: Erasmus University Rotterdam

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Abstract: Robotic mobile fulfillment systems (RMFS) have seen many implementations in recent years, due to their high flexibility and low operational cost. Such a system stores goods in movable shelves and uses movable robots to transport the shelves. The robot is battery powered and the battery depletes during operations, which can seriously affect the performance of the system. This study focuses on battery management problem in an RMFS, considering a battery swapping and a battery charging strategy with plug-in or inductive charging. We build a semi-open queueing network (SOQN) to estimate system performance, modeling the battery charging process as a single queue and the battery swapping process as a nested SOQN. We develop a decomposition method to solve the analytical models and validate them through simulation. Our models can be used to optimize battery recovery strategies and compare their cost and throughput time performance. The results show that throughput time performance can be significantly affected by the battery recovery policy, that inductive charging performs best, and that battery swapping outperforms plug-in charging by as large as 4.88%, in terms of retrieval transaction throughput time. However, the annual cost of the RMFS using the battery swapping strategy is generally higher than that of the RMFS using the plug-in charging strategy. In the RMFS that uses the inductive charging strategy, a critical price of a robot can be found, for a lower robot price and a small required retrieval transaction throughput time, inductive charging outperforms both plug-in charging and battery swapping strategies in terms of annual cost. We also find that ignoring the battery recovery will underestimate the number of robots required and the system cost for more than 15%.

Keywords: Warehousing; Parts-to-picker order picking system; Robotic mobile fulfillment systems; Battery charging; Performance analysis (search for similar items in EconPapers)
Date: 2018-06-01
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Citations: View citations in EconPapers (21)

Published in European Journal of Operational Research, 2018, 267 (2), 733-753 p. ⟨10.1016/j.ejor.2017.12.008⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02312110

DOI: 10.1016/j.ejor.2017.12.008

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