Reference point-based evolutionary multi-objective optimization for industrial systems simulation
2012 (English)In: Proceedings of the 2012 Winter Simulation Conference (WSC) / [ed] C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, IEEE conference proceedings, 2012Conference paper (Refereed)
In Multi-objective Optimization the goal is to present a set of Pareto-optimal solutions to the decision maker (DM). One out of these solutions is then chosen according to the DM preferences. Given that the DM has some general idea of what type of solution is preferred, a more efficient optimization could be run. This can be accomplished by letting the optimization algorithm make use of this preference information and guide the search towards better solutions that correspond to the preferences. One example for such kind of algorithms is the reference point-based NSGA-II algorithm (R-NSGA-II), by which user-specified reference points can be used to guide the search in the objective space and the diversity of the focused Pareto-set can be controlled. In this paper, the applicability of the R-NSGA-II algorithm in solving industrial-scale simulation-based optimization problems is illustrated through a case study of the improvement of a production line.
Place, publisher, year, edition, pages
IEEE conference proceedings, 2012.
, Winter Simulation Conference, ISSN 0891-7736
Research subject Technology
IdentifiersURN: urn:nbn:se:his:diva-7155DOI: 10.1109/WSC.2012.6465130ScopusID: 2-s2.0-84874746413ISBN: 978-1-4673-4781-5ISBN: 978-1-4673-4779-2OAI: oai:DiVA.org:his-7155DiVA: diva2:604031
2012 Winter Simulation Conference, WSC 2012, December 9-12, Berlin