A minimal switching procedure for constrained ranking and selection under independent or common random numbers

Healey, Christopher M.; Andradóttir, Sigrún; Kim, Seong‐Hee

doi:10.1080/0740817x.2015.1009198

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Lesnevski et al [2007] also raises a similar issue about the use of CRN when unequal sample sizes across systems are available in screening. Further study of this topic falls outside the scope of this article, but some methods are presented in Healey et al [2014], who propose heuristic modifications that allow procedures with unequal sample sizes such as HAK to benefit from CRN while satisfying the desired level of PCS.…”

Section: Decisions Under Correlationmentioning

confidence: 99%

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Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling

Healey

Andradóttir

Kim

2014

ACM Trans. Model. Comput. Simul.

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We consider the problem of selecting the best feasible system with constraints on multiple secondary performance measures. We develop fully sequential indifference-zone procedures to solve this problem that guarantee a nominal probability of correct selection. In addition, we address two issues critical to the efficiency of these procedures: namely, the allocation of error between feasibility determination and selection of the best system, and the use of Common Random Numbers. We provide a recommended error allocation as a function of the number of constraints, supported by an experimental study and an approximate asymptotic analysis. The validity and efficiency of the new procedures with independent and CRN are demonstrated through both analytical and experimental results.

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Section: Decisions Under Correlationmentioning

confidence: 99%

“…Andradóttir and Kim [2010] and Healey et al [2013] proposed and analyzed several fully sequential IZ R&S procedures within this framework for independent systems with one constraint. For procedures that try to minimize switching due to the possibly high cost of stopping and restarting complex simulations, see Healey et al [2014].…”

Section: Introductionmentioning

confidence: 99%

Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling

Healey

Andradóttir

Kim

2014

ACM Trans. Model. Comput. Simul.

Self Cite

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“…Healey et al (2014) further developed the work of Andradóttir and Kim (2010) to handle multiple secondary performance measures. Then, Healey et al (2015) reconsidered the problem by taking the switching cost into consideration. Rather than modeling the secondary performance as a Gaussian random vector as all previous works, Hong et al (2015a) perceived the secondary performance as a Bernoulli random variable and the secondary performance measure as a probability.…”

Section: Constrained Randsmentioning

confidence: 99%

Review on ranking and selection: A new perspective

2021

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In this paper, we briefly review the development of ranking and selection (R&S) in the past 70 years, especially the theoretical achievements and practical applications in the past 20 years. Different from the frequentist and Bayesian classifications adopted by Kim and Nelson (2006b) and Chick (2006) in their review articles, we categorize existing R&S procedures into fixed-precision and fixed-budget procedures, as in Hunter and Nelson (2017). We show that these two categories of procedures essentially differ in the underlying methodological formulations, i.e., they are built on hypothesis testing and dynamic programming, respectively. In light of this variation, we review in detail some well-known procedures in the literature and show how they fit into these two formulations. In addition, we discuss the use of R&S procedures in solving various practical problems and propose what we think are the important research questions in the field.

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“…In the first phase (feasibility detection), all the feasible designs are identified and in the second phase (comparison), the best design is selected. This work was later extended in Healey, Andradóttir, and Kim (2014) by considering multiple constraints and common random numbers (CRN) and in Healey, Andradóttir, and Kim (2015) by considering the switching cost. Lee, Pujowidianto, Li, Chen, and Yap (2012) solved the constrained R&S using the OCBA approach and proposed an asymptotic optimal budget allocation rule.…”

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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A minimal switching procedure for constrained ranking and selection under independent or common random numbers

Abstract: discussion of when F I B and MSS satisfy Assumptions 3 and 4, respectively. Then, as CMS satisfies Assumptions 2, 3, and 4, we apply Lemma 1 and the fact that β 1 = β 2 /s, and obtain that

Cited by 9 publications

References 27 publications

Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling

Selection Procedures for Simulations with Multiple Constraints under Independent and Correlated Sampling

Review on ranking and selection: A new perspective

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

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