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Feng, B. ; Pedrielli, G ; Peng, Y. ; Shashaani, S. ; Song, E. ; Corlu, C. ; Lee, L. ; Chew, E. ; Roeder, T. ; Lendermann, P. (Ed.)Ranking&selection (R&S) procedures are simulationoptimization algorithms for making onetime decisions among a finite set of alternative system designs or feasible solutions with a statistical assurance of a good selection. R&S with covariates (R&S+C) extends the paradigm to allow the optimal selection to depend on contextual information that is obtained just prior to the need for a decision. The dominant approach for solving such problems is to employ offline simulation to create metamodels that predict the performance of each system or feasible solution as a function of the covariate. This paper introduces a fundamentally different approach that solves individual R&S problems offline for various values of the covariate, and then treats the realtime decision as a classification problem: given the covariate information, which system is a good solution? Our approach exploits the availability of efficient R&S procedures, requires milder assumptions than the metamodeling paradigm to provide strong guarantees, and can be more efficient.more » « less

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

Feng, B. ; Pedrielli, G ; Peng, Y. ; Shashaani, S. ; Song, E. ; Corlu, C. ; Lee, L. ; Chew, E. ; Roeder, T. ; Lendermann, P. (Ed.)Ranking & selection (R&S) procedures are simulationoptimization algorithms for making onetime decisions among a finite set of alternative system designs or feasible solutions with a statistical assurance of a good selection. R&S with covariates (R&S+C) extends the paradigm to allow the optimal selection to depend on contextual information that is obtained just prior to the need for a decision. The dominant approach for solving such problems is to employ offline simulation to create metamodels that predict the performance of each system or feasible solution as a function of the covariate. This paper introduces a fundamentally different approach that solves individual R&S problems offline for various values of the covariate, and then treats the realtime decision as a classification problem: given the covariate information, which system is a good solution? Our approach exploits the availability of efficient R&S procedures, requires milder assumptions than the metamodeling paradigm to provide strong guarantees, and can be more efficient.more » « less

Feng, B. ; Pedrielli, G ; Peng, Y. ; Shashaani, S. ; Song, E. ; Corlu, C. ; Lee, L. ; Chew, E. ; Roeder, T. ; Lendermann, P. (Ed.)The Rapid Gaussian Markov Improvement Algorithm (rGMIA) solves discrete optimization via simulation problems by using a Gaussian Markov random field and complete expected improvement as the sampling and stopping criterion. rGMIA has been created as a sequential sampling procedure run on a single processor. In this paper, we extend rGMIA to a parallel computing environment when q+1 solutions can be simulated in parallel. To this end, we introduce the qpoint complete expected improvement criterion to determine a batch of q+1 solutions to simulate. This new criterion is implemented in a new objectoriented rGMIA package.more » « less

Feng, B. ; Pedrielli, G ; Peng, Y. ; Shashaani, S. ; Song, E. ; Corlu, C. ; Lee, L. ; Chew, E. ; Roeder, T. ; Lendermann, P. (Ed.)The Rapid Gaussian Markov Improvement Algorithm (rGMIA) solves discrete optimization via simulation problems by using a Gaussian Markov random field and complete expected improvement as the sampling and stopping criterion. rGMIA has been created as a sequential sampling procedure run on a single processor. In this paper, we extend rGMIA to a parallel computing environment when q+1 solutions can be simulated in parallel. To this end, we introduce the qpoint complete expected improvement criterion to determine a batch of q+1 solutions to simulate. This new criterion is implemented in a new objectoriented rGMIA package.more » « less

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.more » « less

Feng, B. ; Pedrielli, G ; Peng, Y. ; Shashaani, S. ; Song, E. ; Corlu, C. G. ; Lee, L.H. ; Chew, E. P. ; Roeder, T. ; Lendermann, P. (Ed.)Automatic differentiation (AD) can provide infinitesimal perturbation analysis (IPA) derivative estimates directly from simulation code. These gradient estimators are simple to obtain analytically, at least in principle, but may be tedious to derive and implement in code. AD software tools aim to ease this workload by requiring little more than writing the simulation code. We review considerations when choosing an AD tool for simulation, demonstrate how to apply some specific AD tools to simulation, and provide insightful experiments highlighting the effects of different choices to be made when applying AD in simulation.more » « less