A Performance Analysis and Optimization Method Based on Petri Net for Manufacturing Execution System

Abstract

The Manufacturing Execution System (MES) is a critical component of intelligent manufacturing systems, enabling the automation and intelligent control of production processes. However, conventional MESs generally lack sufficient flexibility in coping with process reconfiguration and dynamic resource variations, thereby resulting in production bottlenecks and prolonged manufacturing time. To alleviate production bottlenecks while reducing overall manufacturing time, we propose a modeling, analysis, and resource allocation optimization framework for MESs. This framework employs rewritable timed Petri nets (RTPNs) to model and analyze MES behavior. Furthermore, a resource allocation optimization algorithm is developed to minimize production time. A clothes customization manufacturing system is adopted as a case study to demonstrate the effectiveness of the proposed method. The production process is reconstructed and optimized based on the RTPN model, and system performance is validated through simulation. Experimental results indicate that the proposed method significantly reduces production blocking and waiting rates, thereby improving overall operational efficiency.