Hybrid Flow Shop Scheduling through Reinforcement Learning: A systematic literature review

04/04/2025

This paper reviews the application of Reinforcement Learning (RL) in solving Hybrid Flow Shop Scheduling (HFS) problems, a complex manufacturing scheduling challenge. HFS involves processing jobs through multiple stages, each stage has multiple machines that can work in parallel, aiming to optimize objectives like makespan, tardiness, and energy consumption. While traditional methods are well-studied, RL’s in HFS problem is relatively new. The review analyzes 26 studies identified through IEEE Xplore, Scopus, and Web of Science databases (as of April 2024), categorizing them based on RL algorithms, problem types, and objectives. Our analysis reveals the increasing adoption of advanced RL methods like Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to handle the complexities of HFS, often achieving superior performance compared to metaheuristics and scheduling heuristics. Furthermore, we explore the trend of integrating RL with other optimization techniques and discuss the potential for real-world applications, model interpretability, and the consideration of additional constraints and uncertainties. This review provides valuable insights into the current state and future directions in HFS using RL.

Authors in the community:

• 

  F

  Fabio Augusto Faria

  ist430285

AM - Accepted manuscript

ACM

https://www.sigapp.org/sac/sac2025/

The 40th ACM/SIGAPP Symposium On Applied Computing

computer-and-information-sciences - Computer and information sciences

Keywords

• reinforcement learning
• Hybrid Flow Shop
• Scheduling

eng - English