Fast converging, automated experiment runs for material flow simulations using distributed computing and combined metaheuristics

The analysis of production systems using discrete, event-based simulation is wide spread and generally accepted as a decision support technology. It aims either at the comparison of competitive system designs or the identification of a "best possible" parameter configuration of a simulation model. Here, combinatorial techniques of simulation and optimization methods support the user in finding optimal solutions, but typically result in long computation times, which often prohibits a practical application in industry. This paper presents a fast converging procedure as a combination of heuristic approaches, namely Particle Swarm Optimization and Genetic Algorithm, within a material flow simulation to close this gap. Our integrated implementation allows automated, distributed simulation runs for practical, complex production systems. First results show the proof of concept with a reference model and demonstrate the benefits of combinatorial and parallel processing.