Intelligent Layout Reconfiguration for Reconfigurable Assembly System: A Genetic Algorithm Approach

Abstract

The ever-evolving automotive industry landscape, driven by shifting customer demands, necessitates flexible manufacturing solutions. Reconfigurable Manufacturing Systems (RMS), integrating modular facilities and Automated Mobile Robots (AMRs), emerge as pivotal alternatives to inflexible dedicated conveyor systems. This study delves into optimizing layout reconfiguration within automotive assembly, with a specific focus on the Reconfigurable Assembly System (RAS) inheriting the traits of RMS. We are focused on addressing scenarios characterized by frequent production schedule changes, necessitating frequent layout reconfiguration. Our approach prioritizes maintaining high area utilization without compromising throughput. In this study, we modified the NSGA- II algorithm, one of advanced Genetic Algorithms (GA) and proposed a layout reconfiguration algorithm to concurrently optimize two key objectives: (1) area utilization and (2) throughput, crucial facets of layout optimization. The proposed algorithm, integrated with discrete event simulation models spanning six layout scenarios, demonstrates significant enhancement in area utilization without compromising throughput integrity, by confirmed simulation studies.