Perspectives in Operations Research
Papers in Honor of Saul Gass’ 80th Birthday
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Chapter and Conference Paper
A tutorial overview of optimization via discrete-event simulation
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Article
Optimization via simulation: A review
We review techniques for optimizing stochastic discrete-event systems via simulation. We discuss both the discrete parameter case and the continuous parameter case, but concentrate on the latter which has domi...
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Article
Techniques for optimization via simulation: an experimental study on an (s,S) inventory system
Various methods have been proposed to conduct optimization via discrete-event simulation. Prevalent among these are gradient-based algorithms, and more recently, so-called retrospective approaches that determi...
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Article
Optimization of discrete event systems via simultaneous perturbation stochastic approximation
We investigate the use of simultaneous perturbation stochastic approximation for the optimization of discrete-event systems via simulation. Application of stochastic approximation to simulation optimization is...
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Article
Setting thresholds for periodic order release
In job shop manufacturing environments, controlling the release of work orders can significantly improve system performance, especially when a few bottleneck resources limit shop throughput. A periodic order r...
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Chapter
A Simulation-Based Policy Iteration Algorithm for Average Cost Unichain Markov Decision Processes
In this paper, we propose a simulation-based policy iteration algorithm for Markov decision process (MDP) problems with average cost criterion under the unichain assumption, which is a weaker assumption than f...
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Chapter
Two Timescale SPSA Algorithms for Rate-Based ABR Flow Control
Two numerically efficient two timescale simultaneous perturbation stochastic approximation (SPSA) algorithms that use only two simulations are used to obtain optimal structured feedback control policies for ra...
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Reference Work Entry In depth
Simulation optimizationSIMULATION Optimization
Edited by Saul I. Gass and Carl M. Harris
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Reference Work Entry In depth
Perturbation analysis
Edited by Saul I. Gass and Carl M. Harris
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Article
Two-timescale algorithms for simulation optimization of hidden Markov models
We propose two finite difference two-timescale Simultaneous Perturbation Stochastic Approximation (SPSA) algorithms for simulation optimization of hidden Markov models. Stability and convergence of both the al...
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Book
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Chapter
Sensitivity Analysis in Monte Carlo Simulation of Stochastic Activity Networks
Stochastic activity networks (SANs) such as those arising in Project Evaluation Review Technique (PERT) and Critical Path Method (CPM) are an important classical set of models in operations research. We focus ...
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Book
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Book
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Chapter
Variance-Gamma and Monte Carlo
The Variance-Gamma (VG) process was introduced by Dilip B. Madan and Eugene Seneta as a model for asset returns in a paper that appeared in 1990, and subsequently used for option pricing in a 1991 paper by Dil...
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Article
Dynamic sample budget allocation in model-based optimization
Model-based search methods are a class of optimization techniques that search the solution space by sampling from an underlying probability distribution “model,” which is updated iteratively after evaluating t...
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Chapter
A Survey of Some Model-Based Methods for Global Optimization
We review some recent developments of a class of random search methods: model-based methods for global optimization problems. Probability models are used to guide the construction of candidate solutions in mod...
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Book
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Reference Work Entry In depth
Simulation of Stochastic Discrete-Event Systems
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Reference Work Entry In depth
Approximate Dynamic Programming
