P robably one of the most successful interfaces between operations research and computer science has been the development of discrete-event simulation software. The recent integration of optimization techniques into simulation practice, specifically into commercial software, has become nearly ubiquitous, as most discrete-event simulation packages now… (More)
We consider a variation of the subset selection problem in ranking and selection, where motivated by recently developed global optimization approaches applied to simulation optimization, our objective is to identify the top-m out of k designs based on simulated output. Using the optimal computing budget framework, we formulate the problem as that of… (More)
Simultaneous perturbation stochastic approximation (SPSA) algorithms have been found to be very effective for high-dimensional simulation optimization problems. The main idea is to estimate the gradient using simulation output performance measures at only <i>two</i> settings of the <i>N</i>-dimensional parameter vector being optimized rather than at the… (More)
Based on recent results for multiarmed bandit problems, we propose an adaptive sampling algorithm that approximates the optimal value of a finite-horizon Markov decision process (MDP) with finite state and action spaces. The algorithm adaptively chooses which action to sample as the sampling process proceeds and generates an asymptotically unbiased… (More)
The integration of optimization and simulation has become nearly ubiquitous in practice, as most discrete-event simulation packages now include some type of optimization routine. This panel session's objective was to explore the present state of the art in simulation optimization, prevailing issues for researchers, and future prospects for the field. The… (More)
applies and teaches advanced methodologies of design and analysis to solve complex, hierarchical, heterogeneous and dynamic problems of engineering technology and systems for industry and government.
We introduce a new randomized method called Model Reference Adaptive Search (MRAS) for solving global optimization problems. The method works with a parameterized probabilistic model on the solution space and generates at each iteration a group of candidate solutions. These candidate solutions are then used to update the parameters associated with the… (More)
In this tutorial introduction to simulation optimization, we present motivating and illustrative examples, summarize most of the major approaches, and briefly describe some software implementations. The focus is on issues and concepts, rather than mathematical rigor, so the format is Q & A rather than theorem-proof.