This grant provides funding to create a new approach to discrete-event simulation, a computational tool used extensively for design and operational improvement of discrete material flow systems such as factories and warehouses. This new approach combines two existing paradigms. The first is the process-interaction paradigm, which efficiently models flow of material, customers or work through a system, but has limitations in representing complex decision logic. The second is the event-scheduling paradigm, which is used to model complex systems, but is quite costly in modeling time and effort. By classifying discrete material flow systems according to physical and decision logic characteristics, generic decision logic units will be specified for different classes of systems, using event-scheduling to encapsulate decision-making behavior. These units will be interfaced with a process-interaction representation of the physical system flow. The research will experimentally compare results obtained using the new hybrid approach against those obtained using simpler models, in terms of estimates of system performance. Since computation time is a major concern with simulation, the project also will measure the computational effect of having such decision logic units, as compared to a simpler system representation.
Successfully completed, this project and the resulting hybrid approach to simulation modeling methodology will improve the state of the art in modeling and analyzing systems with complex decision logic. One major goal is to provide a better prototyping capability, to test decision logic before it is implemented in today's increasingly complex systems. By comparing the hybrid approach against simpler representations across a variety of system types, an initial understanding of the effect of level of detail in modeling will be achieved. Additional expected results include impact on graduate and undergraduate education, and case studies made available to the research community.
