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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)
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)
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)
We propose a time aggregation approach for the solution of inÿnite horizon average cost Markov decision processes via policy iteration. In this approach, policy update is only carried out when the process visits a subset of the state space. As in state aggregation, this approach leads to a reduced state space, which may lead to a substantial reduction in(More)
" Finite-dimensional regulators for a class of infinite-dimensional systems, " Syst. [13] Q. Vu, " The operator equation AX 0 XB = C with unbounded operators A and B and related abstract Cauchy problems, " Mathematische Abstract—We propose a novel algorithm called evolutionary policy iteration (EPI) for solving infinite horizon discounted reward Markov(More)