Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets

Hunter, Susan R.; Pasupathy, Raghu

doi:10.1287/ijoc.1120.0519

Cited by 77 publications

(53 citation statements)

References 23 publications

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“…Our answer to question Q.1 appears in Section 3 and is a relatively simple extension of recent work by Hunter (2011, and is in general based on the seminal work by Glynn and Juneja (2004). We answer question Q.2 in Section 4, where we demonstrate that the optimal allocation in the proposed setting reduces to a form that is remarkably simple in structure and intuition.…”

Section: Introductionmentioning

confidence: 73%

Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets

Pujowidianto

Pasupathy

Hunter

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

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Consider the context of constrained simulation optimization (SO), that is, optimization problems where the objective function and constraints are known through a Monte Carlo simulation, with corresponding estimators possibly dependent. We identify the nature of sampling plans that characterize efficient algorithms, particularly in large countable spaces. We show that in a certain asymptotic sense, the optimal sampling characterization, that is, the sampling budget for each system that guarantees optimal convergence rates, depends on a single easily estimable quantity called the score. This result provides a useful and easily implementable sampling allocation that approximates the optimal allocation, which is otherwise intractable due to it being the solution to a difficult bilevel optimization problem. Our results point to a simple sequential algorithm for efficiently solving large-scale constrained simulation optimization problems on finite sets.

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Section: Introductionmentioning

confidence: 73%

Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets

Pujowidianto

Pasupathy

Hunter

et al. 2012

Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)

Self Cite

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“…Recent work [95] in the area of DOvS over finite sets provides a quick overview of the field of ranking and selection, and considers general probability distributions and the presence of stochastic constraints simultaneously.…”

Section: Finite Parameter Spacesmentioning

confidence: 99%

Simulation optimization: a review of algorithms and applications

et al. 2014

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Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.

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“…Lee, Pujowidianto, Li, Chen, and Yap (2012) solved the constrained R&S using the OCBA approach and proposed an asymptotic optimal budget allocation rule. Hunter and Pasupathy (2013) and Pasupathy, Hunter, Pujowidianto, Lee, and Chen (2014) further studied this problem from a large-deviations perspective. This method is more flexible in that the underlying random variables can follow a general light-tailed distribution instead of the normal distribution.…”

Section: Introductionmentioning

confidence: 99%

A worst‐case formulation for constrained ranking and selection with input uncertainty

Shi

Gao

Xiao

et al. 2019

Naval Research Logistics

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In this research, we consider robust simulation optimization with stochastic constraints. In particular, we focus on the ranking and selection problem in which the computing time is sufficient to evaluate all the designs (solutions) under consideration. Given a fixed simulation budget, we aim at maximizing the probability of correct selection (PCS) for the best feasible design, where the objective and constraint measures are assessed by their worst‐case performances. To simplify the complexity of PCS, we develop an approximated probability measure and derive the asymptotic optimality condition (optimality condition as the simulation budget goes to infinity) of the resulting problem. A sequential selection procedure is then designed within the optimal computing budget allocation framework. The high efficiency of the proposed procedure is tested via a number of numerical examples. In addition, we provide some useful insights into the efficiency of a budget allocation procedure.

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Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets

Cited by 77 publications

References 23 publications

Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets

Closed-form sampling laws for stochastically constrained simulation optimization on large finite sets

Simulation optimization: a review of algorithms and applications

A worst‐case formulation for constrained ranking and selection with input uncertainty

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