Modelling, Verification, and Optimization of Process Plan Generation in Sustainable Reconfigurable Manufacturing Systems Using Evolutionary-Generated Petri Nets

Abstract

Various evolutionary algorithms have been developed to address environmentally oriented multi-objective process planning challenges in reconfigurable manufacturing systems, though many prioritize effectiveness over flexibility. Research utilizing Petri nets effectively addresses this flexibility issue, with several extensions proposed for modeling complex systems. By leveraging established Petri net theory, the process plan of reconfigurable manufacturing systems (RMS) can be encoded, enabling the optimization of process plan generation and the identification of potential deadlocks. This paper introduces a novel approach to enhance process plan generation, combining Evolutionary Petri Nets (a variant of Petri nets) with the enhanced genetic algorithm N˜SGA-III. The method optimizes four key objectives within the context of Sustainable Reconfigurable Manufacturing Systems (SRMS): total production cost, total production time, greenhouse gas emissions from energy consumption, and hazardous liquid waste generation. Numerical experiments were conducted to validate the effectiveness of this approach.