Stochastically Constrained Ranking and Selection via SCORE

Pasupathy, Raghu; Hunter, Susan R.; Pujowidianto, Nugroho A.; Lee, Loo Hay; Chen, Chun‐Hung

doi:10.1145/2630066

Cited by 79 publications

(24 citation statements)

References 20 publications

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“…They claim, therefore, that the method is a direct competitor to the MOCBA procedure. They mean to achieve the goal by adopting the concept of the SCORE (sampling criteria for optimisation using rate estimators) allocation rule [31], which is a simulation budget allocation rule for constrained simulation optimisation problems.…”

Section: 3mentioning

confidence: 99%

Single- And Multi-Objective Ranking and Selection Procedures in Simulation: A Historical Review

Yoon

Bekker

2017

SAJIE

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Ranking and selection (R&S) procedures form an important research field in computer simulation and its applications. In simulation, one usually has to select the best from a number of scenarios or alternative designs. Often, the simulated processes have a stochastic nature, which means that, to distinguish alternatives, they must exhibit significant statistical differences. R&S procedures assist the decision-maker with the selection of the best alternative with high confidence. This paper reviews past and current R&S procedures. The review traces back to the 1950s, when the first R&S procedure was proposed, and discusses the various R&S procedures proposed since then to the present day, presenting a cursory view of the research in the area. The review includes studies in both the single-objective and the multi-objective domains. It presents the research trend, discusses specific issues, and gives recommendations for future research in both domains. OPSOMMINGRangorde-en keuseprosedures (R&K) vorm 'n belangrike navorsingsveld in rekenaarsimulasie en -toepassings. Simulasiestudies vereis gewoonlik dat die beste kandidaat van 'n aantal scenarios of alternatiewe ontwerpe gekies moet word. Die gesimuleerde prosesse is gewoonlik van 'n stogastiese aard, en hulle moet statisties-beduidend verskil ten einde onderskeid te kan tref. R&K prosedures ondersteun die besluitnemer om die beste alternatief met groot vertroue te kies. Hierdie artikel verskaf 'n resensie van vroeë en huidige R&K prosedures. Die ondersoek strek terug tot in die 1950s toe die eerste R&K prosedures voorgestel is, en bespreek die verskeie R&K prosedures wat sedertdien ontwikkel is, terwyl 'n oorsigtelike blik op die navorsingsveld gegee word. Die resensie sluit studies in beide enkel-en multidoelwitdomein in. Dit bespreek navorsingsneigings en spesifieke kwessies, en maak voorstelle vir verdere navorsing in beide domeins. INTRODUCTIONComputer simulation is a powerful and essential tool in modern society to improve the operation of current systems and business practices. It contributes to the efficient management of processes and systems -the main focus of many industrial engineers -by providing a what-if analysis. In operating a system, one often faces a situation where the following question arises: If some changes were made to the current parameter settings (or decision variables) 1 in the operation of the system, what would happen? Would they result in a better performance of the system or not? It is usually not easy to estimate the effect of the changes because the system is often complex and exhibits a stochastic nature. Simulation helps decision-makers in such cases by simulating the real system and providing the estimates of system performance measures. One can compare the performance of the simulated 1 Various terms are used in simulation to indicate parameter settings of the system: system designs, systems, designs, scenarios, and/or alternatives. The term population is also used in the same context in statistics.

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Section: 3mentioning

confidence: 99%

Single- And Multi-Objective Ranking and Selection Procedures in Simulation: A Historical Review

Yoon

Bekker

2017

SAJIE

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“…This represents a stochastic constraint extension of problem (1). There has been recent work on extending indifference-zone R&S procedures as well as OCBA procedures (Batur and Kim, 2010;Park and Kim, 2011;Lee et al, 2012;Pasupathy et al, 2014) to handle stochastic constraints. Nagarajan and Pasupathy (2013) presented a simulation optimization algorithm that supports stochastic constraints.…”

Section: Stochastic Constraints and Multi-objective Simulation Optimimentioning

confidence: 99%

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Huang

Chen

et al. 2015

Asia Pac. J. Oper. Res.

Self Cite

165

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Recent advances in simulation optimization research and explosive growth in computing power have made it possible to optimize complex stochastic systems that are otherwise intractable. In the first part of this paper, we classify simulation optimization techniques into four categories based on how the search is conducted. We provide tutorial expositions on representative methods from each category, with a focus in recent developments, and compare the strengths and limitations of each category. In the second part of this paper, we review applications of simulation optimization in various contexts, with detailed discussions on health care, logistics, and manufacturing systems. Finally, we explore the potential of simulation optimization in the new era. Specifically, we discuss * Corresponding author. how simulation optimization can benefit from cloud computing and high-performance computing, its integration with big data analytics, and the value of simulation optimization to help address challenges in engineering design of complex systems.

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“…Pasupathy et al (2014) discusses conditions under which it is optimal to allocate most of the budget to x * , and finds that this can happen when the number of systems becomes large. Next, we discuss the modified KG policy of Section 3.2.…”

Section: Ryzhovmentioning

confidence: 99%

Expected improvement is equivalent to OCBA

Ryzhov¹

2015

2015 Winter Simulation Conference (WSC)

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This paper summarizes new theoretical results on the asymptotic sampling rates of expected improvement (EI) methods in fully sequential ranking and selection (R&S). These methods have been widely observed to perform well in practice, and often have asymptotic consistency properties, but rate results are generally difficult to obtain when observations are subject to stochastic noise. We find that, in one general R&S problem, variants of EI produce simulation allocations that are virtually identical to the rate-optimal allocations calculated by the optimal computing budget allocation (OCBA) methodology. This result provides new insight into the good empirical performance of EI under normality assumptions.

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Stochastically Constrained Ranking and Selection via SCORE

Cited by 79 publications

References 20 publications

Single- And Multi-Objective Ranking and Selection Procedures in Simulation: A Historical Review

Single- And Multi-Objective Ranking and Selection Procedures in Simulation: A Historical Review

Simulation Optimization: A Review and Exploration in the New Era of Cloud Computing and Big Data

Expected improvement is equivalent to OCBA

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