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Algorithms
Volume 16
Issue 9
10.3390/a16090408
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Article

A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations

by
Jonas F. Leon
1,2,
Yuda Li
3,
Xabier A. Martin
3,
Laura Calvet
4,
Javier Panadero
5 and
Angel A. Juan
3,*
1
Department of Computer Science, Multimedia and Telecommunication, Universitat Oberta de Catalunya, 08018 Barcelona, Spain
2
Spindox España S.L., Calle Muntaner 305, 08021 Barcelona, Spain
3
Research Center on Production Management and Engineering, Universitat Politècnica de València, Plaza Ferrandiz-Salvador, 03801 Alcoy, Spain
4
Department of Telecommunications & Systems Engineering, Universitat Autònoma de Barcelona, 08202 Sabadell, Spain
5
Department of Computer Architecture & Operating Systems, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain
*
Author to whom correspondence should be addressed.
Algorithms 2023, 16(9), 408; https://doi.org/10.3390/a16090408
Submission received: 21 July 2023 / Revised: 24 August 2023 / Accepted: 25 August 2023 / Published: 27 August 2023
(This article belongs to the Special Issue 2022 and 2023 Selected Papers from Algorithms Editorial Board Members)
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Abstract

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator.
Keywords: warehouse operations; hybrid algorithms; simulation; reinforcement learning; optimization warehouse operations; hybrid algorithms; simulation; reinforcement learning; optimization

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MDPI and ACS Style

Leon, J.F.; Li, Y.; Martin, X.A.; Calvet, L.; Panadero, J.; Juan, A.A. A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations. Algorithms 2023, 16, 408. https://doi.org/10.3390/a16090408

AMA Style

Leon JF, Li Y, Martin XA, Calvet L, Panadero J, Juan AA. A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations. Algorithms. 2023; 16(9):408. https://doi.org/10.3390/a16090408

Chicago/Turabian Style

Leon, Jonas F., Yuda Li, Xabier A. Martin, Laura Calvet, Javier Panadero, and Angel A. Juan. 2023. "A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations" Algorithms 16, no. 9: 408. https://doi.org/10.3390/a16090408

APA Style

Leon, J. F., Li, Y., Martin, X. A., Calvet, L., Panadero, J., & Juan, A. A. (2023). A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations. Algorithms, 16(9), 408. https://doi.org/10.3390/a16090408

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