A Review on Reinforcement Learning in Production Scheduling: An Inferential Perspective

Author:

Modrak Vladimir1ORCID,Sudhakarapandian Ranjitharamasamy2ORCID,Balamurugan Arunmozhi2,Soltysova Zuzana1ORCID

Affiliation:

1. Faculty of Manufacturing Technologies, Technical University of Kosice, 080 01 Prešov, Slovakia

2. School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India

Abstract

In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of pertinent publications contained in Scopus, ScienceDirect, and Google Scholar databases. The study focuses on a wide spectrum of publications spanning the years between 1996 and 2024. The findings of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

Funder

European Union’s Horizon research and innovation program

Ministry of Education of the Slovak Republic

Publisher

MDPI AG

Reference111 articles.

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3. Xhafa, F., and Abraham, A. (2008). Metaheuristics for Scheduling in Industrial and Manufacturing Applications, Springer.

4. Particle swarm optimization for constrained instruction scheduling;VLSI Des.,2008

5. A Modified Heuristics for the Batch Size Optimization with Combined Time in a Mass-Customized Manufacturing System;Balamurugan;Int. J. Ind. Eng. Theory Appl. Pract.,2023

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