COMPOSABLE MODELING AND DISTRIBUTED SIMULATION FRAMEWORK FOR DISCRETE SUPPLY-CHAIN SYSTEMS WITH PREDICTIVE CONTROL by

Supply-chain networks such as semiconductor manufacturing systems exhibit a high degree of structural and behavioral complexity. Simulation modeling concepts, approaches, and tools are the primary means for analysis and design of intricate behavior and relationships found in many of today’s supply-chain networks. A fundamental barrier in developing rigorous simulation models of supply-chain systems is the necessity of using inherently different kinds of models and simulators. This is because no single modeling and simulation framework has been shown to adequately represent, at a realistic level of detail, a supplychain system with tactical (short-term) control and strategic (long-term) planning policies. Composition of disparate model types affords rigorous synthesis of complementary classes of simulation, control, and optimization models. A novel framework using an approach called Knowledge Interchange Broker (KIB) was developed for composing the distinct classes of Discrete Event System Specification (DEVS), Model Predictive Control (MPC), and Linear Optimization (LP) models. First, the KIB model composability approach was employed to compose DEVS and MPC modeling formalisms. A KIBDEVS/MPC was developed and used to create a hybrid DEVSJAVA/MATLAB prototype environment. The benefits of simulating combined discrete-event and control-theoretic models was demonstrated against a scaled prototypical semiconductor supply-chain system. Then, the KIBDEVS/LP/MPC was developed to support composing models that can be described in DEVS, MPC, and LP modeling formalism. This novel KIB provides a set of suitable message mappings and transformations. A causal parallel execution protocol with logical time synchronization was devised and used to develop a prototype distributed simulation framework for DEVSJAVA, MATLAB, and OPLStudio, a linear optimization tool. The resulting simulation framework