Agent-based simulation and optimization of hybrid flow shop considering multi-skilled workers and fatigue factors. (April 2023)

• Record Type: Journal Article
• Authors: Liu, Youshan; Shen, Weiming; Zhang, Chunjiang; Sun, Xueyan
• Is Part Of: Robotics and computer-integrated manufacturing. Volume 80(2023)
• Publication Date: 2023-04
• Subjects: Agent-based simulation -- Simulation-based optimization -- Hybrid flow shop -- Worker fatigue modeling -- Reinforcement learning; Robots, Industrial -- Periodicals; Computer integrated manufacturing systems -- Periodicals; Robotics -- Periodicals; Robots industriels -- Périodiques; Productique -- Périodiques; Robotique -- Périodiques; 670.285
• DOI: 10.1016/j.rcim.2022.102478
• Languages: English
• ISSNs: 0736-5845

Abstract

Abstract: In the past few decades, more and more studies have begun to consider the impact of human factors on manufacturing systems. This paper studies a hybrid flow shop scheduling problem considering multi-skilled workers and fatigue factors. An agent-based simulation system is established to cope with the uncertainties in the worker fatigue model. Furthermore, this paper proposes a novel simulation-based optimization (SBO) framework, which combines genetic algorithm (GA) and reinforcement learning (RL) to address the hybrid flow shop scheduling problem. Numerical experiments are conducted on several instances with different production configurations. In particular, a pharmaceutical production facility is modeled as a hybrid flow shop to demonstrate the feasibility and effectiveness of the proposed SBO method. Highlights: Worker fatigue and skill levels are considered in the modeling of hybrid flow shop. Parameters of the fatigue model are considered as uncertain factors. Agent-based simulation is combined reinforcement learning and meta-heuristics. A simulation-based optimization framework is developed.