Journal ArticleDOI
Discrete Optimization via Simulation Using COMPASS
L. Jeff Hong,Barry L. Nelson +1 more
TLDR
In this article, an optimization-via-simulation algorithm, called COMPASS, was proposed for estimating the performance measure via a stochastic, discrete-event simulation, and the decision variables were integer ordered.Abstract:
We propose an optimization-via-simulation algorithm, called COMPASS, for use when the performance measure is estimated via a stochastic, discrete-event simulation, and the decision variables are integer ordered. We prove that COMPASS converges to the set of local optimal solutions with probability 1 for both terminating and steady-state simulation, and for both fully constrained problems and partially constrained or unconstrained problems under mild conditions.read more
Citations
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Journal ArticleDOI
Simulation for manufacturing system design and operation: Literature review and analysis
Ashkan Negahban,Jeffrey S. Smith +1 more
TL;DR: A comprehensive review of discrete event simulation publications published between 2002 and 2013 with a particular focus on applications in manufacturing is provided in this paper, where the literature is classified into three general classes of manufacturing system design, manufacturing system operation, and simulation language/package development.
Journal ArticleDOI
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.
Journal ArticleDOI
A unified framework for stochastic optimization
TL;DR: It is argued that the principles of bandit problems should become a core dimension of mainstream stochastic optimization, and a universal modeling framework is proposed that encompasses all of these competing approaches.
Journal ArticleDOI
Practical Heteroscedastic Gaussian Process Modeling for Large Simulation Experiments
TL;DR: A unified view of likelihood based Gaussian progress regression for simulation experiments exhibiting input-dependent noise is presented, and a latent-variable idea from machine learning is borrowed to address heteroscedasticity, thereby simultaneously leveraging the computational and statistical efficiency of designs with replication.
References
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Book
Probability and Measure
TL;DR: In this paper, the convergence of distributions is considered in the context of conditional probability, i.e., random variables and expected values, and the probability of a given distribution converging to a certain value.
Book
Large Deviations Techniques and Applications
Amir Dembo,Ofer Zeitouni +1 more
TL;DR: The LDP for Abstract Empirical Measures and applications-The Finite Dimensional Case and Applications of Empirically Measures LDP are presented.
Journal ArticleDOI
The Sample Average Approximation Method for Stochastic Discrete Optimization
TL;DR: A Monte Carlo simulation--based approach to stochastic discrete optimization problems, where a random sample is generated and the expected value function is approximated by the corresponding sample average function.
Journal ArticleDOI
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
TL;DR: This paper presents a new approach that can further enhance the efficiency of ordinal optimization, which determines a highly efficient number of simulation replications or samples and significantly reduces the total simulation cost.