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1. Posterior-Based Stopping Rules for Bayesian Ranking-and-Selection Procedures

   https://doi.org/10.1287/ijoc.2021.1132  

   Eckman, David J.; Henderson, Shane G. (May 2022, INFORMS Journal on Computing)

   Sequential ranking-and-selection procedures deliver Bayesian guarantees by repeatedly computing a posterior quantity of interest—for example, the posterior probability of good selection or the posterior expected opportunity cost—and terminating when this quantity crosses some threshold. Computing these posterior quantities entails nontrivial numerical computation. Thus, rather than exactly check such posterior-based stopping rules, it is common practice to use cheaply computable bounds on the posterior quantity of interest, for example, those based on Bonferroni’s or Slepian’s inequalities. The result is a conservative procedure that samples more simulation replications than are necessary. We explore how the time spent simulating these additional replications might be better spent computing the posterior quantity of interest via numerical integration, with the potential for terminating the procedure sooner. To this end, we develop several methods for improving the computational efficiency of exactly checking the stopping rules. Simulation experiments demonstrate that the proposed methods can, in some instances, significantly reduce a procedure’s total sample size. We further show these savings can be attained with little added computational effort by making effective use of a Monte Carlo estimate of the posterior quantity of interest. Summary of Contribution: The widespread use of commercial simulation software in industry has made ranking-and-selection (R&S) algorithms an accessible simulation-optimization tool for operations research practitioners. This paper addresses computational aspects of R&S procedures delivering finite-time Bayesian statistical guarantees, primarily the decision of when to terminate sampling. Checking stopping rules entails computing or approximating posterior quantities of interest perceived as being computationally intensive to evaluate. The main results of this paper show that these quantities can be efficiently computed via numerical integration and can yield substantial savings in sampling relative to the prevailing approach of using conservative bounds. In addition to enhancing the performance of Bayesian R&S procedures, the results have the potential to advance other research in this space, including the development of more efficient allocation rules. 

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2. EVALUATION OF bi-PASS FOR PARALLEL SIMULATION OPTIMIZATION

   Pei, L; Nelson, B L; Hunter, S R (December 2020, Proceedings of the Winter Simulation Conference)

   Bae, K-H; Feng, B; Kim, S; Lazarova-Molnar, S; Zheng, Z; Roeder, T; Thiesing, R (Ed.)

   Cheap parallel computing has greatly extended the reach of ranking & selection (R&S) for simulation optimization. In this paper we present an evaluation of bi-PASS, a R&S procedure created specifically for parallel implementation and very large numbers of system designs. We compare bi-PASS to the state-ofthe- art Good Selection Procedure and an easy-to-implement subset selection procedure. This is one of the few papers to consider both computational and statistical comparison of parallel R&S procedures. 

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3. Selection of the Best in the Presence of Subjective Stochastic Constraints

   https://doi.org/10.1145/3664814  

   Zhou, Yuwei; Andradóttir, Sigrún; Kim, Seong-Hee (October 2024, ACM Transactions on Modeling and Computer Simulation)

   We consider the problem of finding a system with the best primary performance measure among a finite number of simulated systems in the presence of subjective stochastic constraints on secondary performance measures. When no feasible system exists, the decision maker may be willing to relax some constraint thresholds. We take multiple threshold values for each constraint as a user’s input and propose indifference-zone procedures that perform the phases of feasibility check and selection-of-the-best sequentially or simultaneously. Given that there is no change in the underlying simulated systems, our procedures recycle simulation observations to conduct feasibility checks across all potential thresholds. We prove that the proposed procedures yield the best system in the most desirable feasible region possible with at least a pre-specified probability. Our experimental results show that our procedures perform well with respect to the number of observations required to make a decision, as compared with straight-forward procedures that repeatedly solve the problem for each set of constraint thresholds, and that our simultaneously-running procedure provides the best overall performance. 

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4. Let's Do Ranking & Selection

   Nelson, Barry L (December 2022, Proceedings of the 2023 Winter Simulation Conference)

   B. Feng, B; G. Pedrielli, G; Peng, Y; Shashaani, S.; Song, E.; Corlu, C.; Lee, L.; Chew, E.; Roeder, T.; Lendermann, P. (Ed.)

   Many tutorials and survey papers have been written on ranking & selection because it is such a useful tool for simulation optimization when the number of feasible solutions or “systems” is small enough that all of them can be simulated. Cheap, ubiquitous, parallel computing has greatly increased the “all of them can be simulated” limit. Naturally these tutorials and surveys have focused on the underlying theory of R&S and have provided pseudocode procedures. This tutorial, by contrast, emphasizes applications, programming and interpretation of R&S, using the R programming language for illustration. Readers (and the audience) can download the code and follow along with the examples, but no experience with R is needed. 

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5. Let’s Do Ranking & Selection

   Nelson, Barry L. (December 2022, Proceedings of the 2022 Winter Simulation Conference)

   Feng, B.; Pedrielli, G; Peng, Y.; Shashaani, S.; Song, E.; Corlu, C.; Lee, L.; Chew, E.; Roeder, T.; Lendermann, P. (Ed.)

   Many tutorials and survey papers have been written on ranking & selection because it is such a useful tool for simulation optimization when the number of feasible solutions or “systems” is small enough that all of them can be simulated. Cheap, ubiquitous, parallel computing has greatly increased the “all of them can be simulated” limit. Naturally these tutorials and surveys have focused on the underlying theory of R&S and have provided pseudocode procedures. This tutorial, by contrast, emphasizes applications, programming and interpretation of R&S, using the R programming language for illustration. Readers (and the audience) can download the code and follow along with the examples, but no experience with R is needed. 

   more » « less