Hybrid GA/MSDLO Approach to Solve a Greenfield Robotized Machine Tending Layout

Abstract

Meta-heuristics like genetic algorithms (GAs) effectively explore solution spaces but often yield suboptimal results and are sensitive to parameter tuning. In contrast, the mass-spring-damper layout optimization (MSDLO) method is fast and excels in locally optimizing resource arrangements, though its effectiveness depends on the initial resource placement. This paper first employs genetic algorithms to establish an optimal resource placement in a greenfield layout. We then apply MSDLO to fine-tune the positions and orientations of these resources. The results are presented and discussed, highlighting this approach's practicality in effectively addressing industry needs.