Multi-objective optimization and formal specification of reconfigurable manufacturing system using adaptive NSGA-II

Abstract

Reconfigurable Manufacturing Systems (RMSs) provide new abilities to build dynamic systems with changeable structure at runtime. RMSs are the suitable solution for unexpected breakdown of machines as well as the dynamic change of markets requirements. One critical and important feature in the RMSs is their ability to update machines and to select the most optimal configuration at any time of the production system life. The choice of the configuration must guarantee both objectives: optimization of resources and preservation of the best properties after every reconfiguration. In this paper, we propose a hybrid approach which combines Genetic Algorithms (GAs) and High Level Petri Nets (HLPNs) in a unique formalism to tackle with the both previous objectives. The paper presents the formalization of the approach and it demonstrates its feasibility on an RMS case study.