A fully sequential procedure for indifference-zone selection in simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian methods: bayesian methods for simulation
Proceedings of the 32nd conference on Winter simulation
New results on procedures that select the best system using CRN
Proceedings of the 32nd conference on Winter simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Statistical analysis of simulation output: output data analysis for simulations
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Selecting the best system: selecting the best system: theory and methods
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 35th conference on Winter simulation: driving innovation
Proceedings of the 35th conference on Winter simulation: driving innovation
Special topics on simulation analysis: better-than-optimal simulation run allocation?
Proceedings of the 35th conference on Winter simulation: driving innovation
Efficient simulation procedures: comparison with a standard via fully sequential procedures
Proceedings of the 35th conference on Winter simulation: driving innovation
Comparison with a standard via fully sequential procedures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian methods for discrete event simulation
WSC '04 Proceedings of the 36th conference on Winter simulation
Simulation optimization: a review, new developments, and applications
WSC '05 Proceedings of the 37th conference on Winter simulation
Review of advanced methods for simulation output analysis
WSC '05 Proceedings of the 37th conference on Winter simulation
New developments in ranking and selection: an empirical comparison of the three main approaches
WSC '05 Proceedings of the 37th conference on Winter simulation
Finding the best in the presence of a stochastic constraint
WSC '05 Proceedings of the 37th conference on Winter simulation
Application of multi-objective simulation-optimization techniques to inventory management problems
WSC '05 Proceedings of the 37th conference on Winter simulation
Bayesian ideas and discrete event simulation: why, what and how
Proceedings of the 38th conference on Winter simulation
Simulation selection problems: overview of an economic analysis
Proceedings of the 38th conference on Winter simulation
Proceedings of the 38th conference on Winter simulation
Simulation Allocation for Determining the Best Design in the Presence of Correlated Sampling
INFORMS Journal on Computing
Selection Procedures with Frequentist Expected Opportunity Cost Bounds
Operations Research
A framework for locally convergent random-search algorithms for discrete optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Extension of the direct optimization algorithm for noisy functions
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
Indifference-zone subset selection procedures: using sample means to improve efficiency
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
On selecting the best individual in noisy environments
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Differentiated service inventory optimization using nested partitions and MOCBA
Computers and Operations Research
Some topics for simulation optimization
Proceedings of the 40th Conference on Winter Simulation
A preliminary study of optimal splitting for rare-event simulation
Proceedings of the 40th Conference on Winter Simulation
A new perspective on feasibility determination
Proceedings of the 40th Conference on Winter Simulation
Update on economic approach to simulation selection problems
Proceedings of the 40th Conference on Winter Simulation
The knowledge-gradient stopping rule for ranking and selection
Proceedings of the 40th Conference on Winter Simulation
Economic Analysis of Simulation Selection Problems
Management Science
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
Finding probably best systems quickly via simulations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Industrial strength COMPASS: A comprehensive algorithm and software for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation optimization using the cross-entropy method with optimal computing budget allocation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Bayesian Simulation and Decision Analysis: An Expository Survey
Decision Analysis
Sequential Sampling to Myopically Maximize the Expected Value of Information
INFORMS Journal on Computing
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Information Collection on a Graph
Operations Research
A brief introduction to optimization via simulation
Winter Simulation Conference
Simulation model calibration with correlated knowledge-gradients
Winter Simulation Conference
Do mean-based ranking and selection procedures consider systems' risk?
Winter Simulation Conference
The conjunction of the knowledge gradient and the economic approach to simulation selection
Winter Simulation Conference
Simulation optimization with hybrid golden region search
Winter Simulation Conference
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
INFORMS Journal on Computing
Hierarchical Knowledge Gradient for Sequential Sampling
The Journal of Machine Learning Research
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
Sequential Sampling with Economics of Selection Procedures
Management Science
Integrating particle swarm optimization with reinforcement learning in noisy problems
Proceedings of the 14th annual conference on Genetic and evolutionary computation
A Framework for Selecting a Selection Procedure
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Optimization via simulation with Bayesian statistics and dynamic programming
Proceedings of the Winter Simulation Conference
Efficient discrete optimization via simulation using stochastic kriging
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
Proceedings of the Winter Simulation Conference
May the best man win: simulation optimization for match-making in e-sports
Proceedings of the Winter Simulation Conference
Best-subset selection procedure
Proceedings of the Winter Simulation Conference
Guessing preferences: a new approach to multi-attribute ranking and selection
Proceedings of the Winter Simulation Conference
A minimal switching procedure for constrained ranking and selection
Proceedings of the Winter Simulation Conference
Performance measures for ranking and selection procedures
Proceedings of the Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
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Standard "indifference-zone" procedures that allocate computer resources to infer the best of a finite set of simulated systems are designed with a statistically conservative, least favorable configuration assumption consider the probability of correct selection (but not the opportunity cost) and assume that the cost of simulating each system is the same. Recent Bayesian work considers opportunity cost and shows that an average case analysis may be less conservative but assumes a known output variance, an assumption that typically is violated in simulation. This paper presents new two-stage and sequential selection procedures that integrate attractive features of both lines of research. They are derived assuming that the simulation output is normally distributed with unknown mean and variance that may differ for each system. We permit the reduction of either opportunity cost loss or the probability of incorrect selection and allow for different replication costs for each system. The generality of our formulation comes at the expense of difficulty in obtaining exact closed-form solutions. We therefore derive a bound for the expected loss associated potentially incorrect selections, then asymptotically minimize that bound. Theoretical and empirical results indicate that our approach compares favorably with indifference-zone procedures.