Integrated Decision Making for Planning and control of Distributed manufacturing Enterprises using Dynamic-Data-Driven adaptive Multi-Scale simulations (DDDAMS)

Discrete-event simulation has become one of the mos t widely used analysis tools for large-scale, complex and dynamic systems such a s supply chains as it can take randomness into account and address very detailed m odels. However, there are major challenges that are faced in simulating such system s, especially when they are used to support short-term decisions ( e.g., operational decisions or maintenance and scheduli ng decisions considered in this research). First, a d et iled simulation requires significant amounts of computation time. Second, given the eno rmous amount of dynamicallychanging data that exists in the system, informatio n needs to be updated wisely in the model in order to prevent unnecessary usage of comp uting and networking resources. Third, there is a lack of methods allowing dynamic data updates during the simulation execution. Overall, in a simulation-based planning and control framework, timely monitoring, analysis, and control is important not t disrupt a dynamically changing system. To meet this temporal requirement and addr ess the above mentioned challenges, a Dynamic-Data-Driven Adaptive Multi-Scale Simulati on (DDDAMS) paradigm is proposed to adaptively adjust the fidelity of a sim ulation model against available computational resources by incorporating dynamic da ta into the executing model, which then steers the measurement process for selective d ata update. To the best of our knowledge, the proposed DDDAMS methodology is one o f the first efforts to present a coherent integrated decision making framework for t imely planning and control of distributed manufacturing enterprises.