Hachicha, Wafik and Ammeri, Ahmed and Masmoudi, Faouzi and Chachoub, Habib (2010): A comprehensive literature classification of simulation optimisation methods. Published in: International Conference on Multiple Objective Programming and Goal Programming  MOPGP10 No. May 24 26, 2010  Sousse  Tunisia

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Abstract
Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single performance measure
Item Type:  MPRA Paper 

Original Title:  A comprehensive literature classification of simulation optimisation methods 
Language:  English 
Keywords:  Simulation Optimization, classification methods, literature survey 
Subjects:  C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics > C44  Operations Research ; Statistical Decision Theory C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61  Optimization Techniques ; Programming Models ; Dynamic Analysis C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General Z  Other Special Topics > Z1  Cultural Economics ; Economic Sociology ; Economic Anthropology > Z11  Economics of the Arts and Literature 
Item ID:  27652 
Depositing User:  Wafik HACHICHA 
Date Deposited:  26 Dec 2010 19:44 
Last Modified:  28 Sep 2019 17:50 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/27652 