Performance Analysis of Multi-Tote Storage and Retrieval Autonomous Mobile Robot Systems

Zhizhen Qin (), Peng Yang (), Yeming Gong () and René B. M. de Koster ()
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Zhizhen Qin: Division of Logistics and Transportation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; Institution of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Peng Yang: Division of Logistics and Transportation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; Institution of Data and Information, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Yeming Gong: Artificial Intelligence in Management Institute, Emlyon Business School, 69130 Écully, France
René B. M. de Koster: Rotterdam School of Management, Erasmus University Rotterdam, 3062 PA Rotterdam, Netherlands

Transportation Science, 2024, vol. 58, issue 5, 1033-1055

Abstract: Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints.

Keywords: multi-tote autonomous mobile robot; mixed storage and retrieval order picking problem; dynamic programming; queuing model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:58:y:2024:i:5:p:1033-1055

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