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

Author:

Leon Jonas F.12ORCID,Li Yuda3ORCID,Martin Xabier A.3ORCID,Calvet Laura4ORCID,Panadero Javier5ORCID,Juan Angel A.3ORCID

Affiliation:

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

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.

Funder

European Commission

Industrial Doctorate Program of the Catalan Government

Investigo Program of the Generalitat Valenciana

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

Reference59 articles.

1. Modeling and Simulation in Intelligent Manufacturing;Zhang;Comput. Ind.,2019

2. Leon, J.F., Li, Y., Peyman, M., Calvet, L., and Juan, A.A. (2023). A Discrete-Event Simheuristic for Solving a Realistic Storage Location Assignment Problem. Mathematics, 11.

3. Sutton, R.S., and Barto, A.G. (2018). Reinforcement Learning: An Introduction, MIT Press.

4. Reinforcement Learning for Logistics and Supply Chain Management: Methodologies, State of the Art, and Future Opportunities;Yan;Transp. Res. Part E Logist. Transp. Rev.,2022

5. Chick, S., Sánchez, P.J., Ferrin, D., and Morrice, D.J. (2002, January 8–11). FlexSim Simulation Environment. Proceedings of the Winter Simulation Conference, San Diego, CA, USA.

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