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

Jonas F. Leon, Yuda Li, Xabier A. Martin, Laura Calvet, Javier Panadero, Angel A. Juan*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number408
Number of pages22
JournalAlgorithms
Volume16
Issue number9
DOIs
Publication statusPublished - Sept 2023

Keywords

  • hybrid algorithms
  • optimization
  • reinforcement learning
  • simulation
  • warehouse operations

Fingerprint

Dive into the research topics of 'A Hybrid Simulation and Reinforcement Learning Algorithm for Enhancing Efficiency in Warehouse Operations'. Together they form a unique fingerprint.

Cite this