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- W2153508610 abstract "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." @default.
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- W2153508610 date "2012-12-09" @default.
- W2153508610 modified "2023-10-03" @default.
- W2153508610 title "Fast converging, automated experiment runs for material flow simulations using distributed computing and combined metaheuristics" @default.
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- W2153508610 doi "https://doi.org/10.5555/2429759.2430103" @default.
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