Extending a factory-based discrete event simulator for parallel optimization of production and supply chain level

Factory-based discrete event simulation (FB-DES) tools are primarily used to model production systems at the factory level. However, integrating factory activities with supply network optimization has been challenging due to data barriers, leaving the application of FB-DES tools in supply chain (SC) contexts underexplored. This study addresses this limitation by enhancing the capabilities of FB-DES tools to integrate network and factory simulations within a single framework. By incorporating genetic algorithms (GAs) into the simulation model we present an approach to optimize production and supply networks based on key performance metrics such as production cost, time, and resource efficiency. The model features an automated scenario generation system using SimTalk methods and Python scripts to simulate and test potential production scenarios continuously. Additionally, Google Cloud Services (Google Maps API) are utilized for more accurate transportation time calculations, further improving network planning capabilities. This comprehensive simulation framework bridges the gap between factory and SC simulation methods, enhancing performance at both levels. This paper highlights the transformative potential of DES tools augmented with GAs and real-time data, paving the way for more resilient supply and production networks.