On the scalability of meta-models in simulation-based optimization of production systems
2015 (English)In: Proceedings of the 2015 Winter Simulation Conference / [ed] L. Yilmaz, W. K. V. Chan, I. Moon, T. M. K. Roeder, C. Macal, and M. D. Rossetti, Piscataway, NJ: IEEE Press, 2015, 3644-3655 p.Conference paper, Presentation (Refereed)
Optimization of production systems often involves numerous simulations of computationally expensive discrete-event models. When derivative-free optimization is sought, one usually resorts to evolutionary and other population-based meta-heuristics. These algorithms typically demand a large number of objective function evaluations, which in turn, drastically increases the computational cost of simulations. To counteract this, meta-models are used to replace expensive simulations with inexpensive approximations. Despite their widespread use, a thorough evaluation of meta-modeling methods has not been carried out yet to the authors' knowledge. In this paper, we analyze 10 different meta-models with respect to their accuracy and training time as a function of the number of training samples and the problem dimension. For our experiments, we choose a standard discrete-event model of an unpaced flow line with scalable number of machines and buffers. The best performing meta-model is then used with an evolutionary algorithm to perform multi-objective optimization of the production model.
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2015. 3644-3655 p.
Simulation, Optimization, Production, Evolutionary
Other Mechanical Engineering
Research subject Technology
IdentifiersURN: urn:nbn:se:his:diva-11917ISBN: 978-1-4673-9743-8OAI: oai:DiVA.org:his-11917DiVA: diva2:902854
WSC '15 Winter Simulation Conference, Huntington Beach, CA, USA — December 06 - 09, 2015