Journal ArticleDOI
Universal alignment probabilities and subset selection for ordinal optimization
T. W. Edward Lau,Yu-Chi Ho +1 more
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TLDR
This paper examines in this paper the subset selection procedure in the context of ordinal optimization introduced in Ref. 1 with the suggestion of quantifiable subset selection sizes which are universally applicable to many simulation and modeling problems.Abstract:
We examine in this paper the subset selection procedure in the context of ordinal optimization introduced in Ref. 1. Major concepts including goal softening, selection subset, alignment probability, and ordered performance curve are formally introduced. A two-parameter model is devised to calculate alignment probabilities for a wide range of cases using two different selection rules: blind pick and horse race. Our major result includes the suggestion of quantifiable subset selection sizes which are universally applicable to many simulation and modeling problems, as demonstrated by the examples in this paper.read more
Citations
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Distribution System Planning With Incorporating DG Reactive Capability and System Uncertainties
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Proceedings of the 2000 winter simulation conference
TL;DR: This paper reviews statistical methods for analyzing output data from computer simulations to find the best system among a set of competing alternatives on the estimation of steady-state system parameters.
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Simulation optimization: a review of algorithms and applications
TL;DR: Simulation optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation as discussed by the authors, where discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise, etc.
Proceedings ArticleDOI
A survey of simulation optimization techniques and procedures
TL;DR: A brief survey of the literature on discrete event simulation optimization over the past decade (1988 to the present) is presented.
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Simulation optimization: a comprehensive review on theory and applications
Eylem Tekin,Ihsan Sabuncuoglu +1 more
TL;DR: This paper presents a comprehensive survey on techniques for simulation optimization with emphasis given on recent developments, and classify the existing techniques according to problem characteristics such as shape of the response surface, objective functions, and parameter spaces.
References
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Journal ArticleDOI
Ordinal Optimization of DEDS
TL;DR: It is argued that cardinal rather than cardinal optimization, i.e., concentrating on finding good, better, or best designs rather than on estimating accurately the performance value of these designs, offers a new, efficient, and complementary approach to the performance optimization of systems.
Book
Theory of Global Random Search
TL;DR: This paper presents an overview of the global optimization problem, a survey of the approaches for its solution, and some ways of applying statistical procedures to construct global random search algorithms.
Book
Multiple decision procedures : theory and methodology of selecting and ranking populations
TL;DR: This book can serve as a text for a graduate topics course in ranking and selection (as it has done at Purdue University for more than 30 years) and will also serve as an valuable reference for researchers and practitioners in various fields, such as agriculture, industry, engineering, and behavioral sciences.