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On the scalability ...
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Bandaru, Sunith,1984-Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Simulation-Based Optimization
(author)
On the scalability of meta-models in simulation-based optimization of production systems
- Article/chapterEnglish2015
Publisher, publication year, extent ...
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Piscataway, NJ :IEEE Press,2015
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LIBRIS-ID:oai:DiVA.org:his-11917
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https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11917URI
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https://doi.org/10.1109/WSC.2015.7408523DOI
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Language:English
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Summary in:English
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Subject category:ref swepub-contenttype
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Subject category:kon swepub-publicationtype
Notes
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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.
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Ng, Amos H. C.Högskolan i Skövde,Institutionen för ingenjörsvetenskap,Forskningscentrum för Virtuella system,Simulation-Based Optimization(Swepub:his)ngam
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Högskolan i SkövdeInstitutionen för ingenjörsvetenskap
(creator_code:org_t)
Related titles
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In:Proceedings of the 2015 Winter Simulation ConferencePiscataway, NJ : IEEE Press, s. 3644-36559781467397438
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