Stochastic discrete optimization
SIAM Journal on Control and Optimization
A method for discrete stochastic optimization
Management Science
Accelerating the convergence of random search methods for discrete stochastic optimization
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Stochastic Comparison Algorithm for Discrete Optimization with Estimation
SIAM Journal on Optimization
Simulation Budget Allocation for Further Enhancing theEfficiency of Ordinal Optimization
Discrete Event Dynamic Systems
New Two-Stage and Sequential Procedures for Selecting the Best Simulated System
Operations Research
Stocking Retail Assortments Under Dynamic Consumer Substitution
Operations Research
Perspectives on the Evolution of Simulation
Operations Research
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
A combined procedure for optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Search and selection for large-scale stochastic optimization
Search and selection for large-scale stochastic optimization
Comparisons with a Standard in Simulation Experiments
Management Science
Using Ranking and Selection to "Clean Up" after Simulation Optimization
Operations Research
Discrete optimization via simulation: algorithms and error control
Discrete optimization via simulation: algorithms and error control
Comparison with a standard via fully sequential procedures
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Simulation optimization using balanced explorative and exploitative search
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
Simulation optimization with countably infinite feasible regions: Efficiency and convergence
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Discrete Optimization via Simulation Using COMPASS
Operations Research
A framework for locally convergent random-search algorithms for discrete optimization via simulation
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization
INFORMS Journal on Computing
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
A brief introduction to optimization via simulation
Winter Simulation Conference
iPOEM: a GPS tool for integrated management in virtualized data centers
Proceedings of the 8th ACM international conference on Autonomic computing
Speeding up COMPASS for high-dimensional discrete optimization via simulation
Operations Research Letters
Efficient discrete optimization via simulation using stochastic kriging
Proceedings of the Winter Simulation Conference
Production planning for semiconductor manufacturing via simulation optimization
Proceedings of the Winter Simulation Conference
Multi-objective COMPASS for discrete optimization via simulation
Proceedings of the Winter Simulation Conference
Optimization via simulation using Gaussian process-based search
Proceedings of the Winter Simulation Conference
Empirical stochastic branch-and-bound for optimization via simulation
Proceedings of the Winter Simulation Conference
An Adaptive Hyperbox Algorithm for High-Dimensional Discrete Optimization via Simulation Problems
INFORMS Journal on Computing
Rapid Screening Procedures for Zero-One Optimization via Simulation
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Stochastic resource allocation using a predictor-based heuristic for optimization via simulation
Computers and Operations Research
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Industrial Strength COMPASS (ISC) is a particular implementation of a general framework for optimizing the expected value of a performance measure of a stochastic simulation with respect to integer-ordered decision variables in a finite (but typically large) feasible region defined by linear-integer constraints. The framework consists of a global-search phase, followed by a local-search phase, and ending with a “clean-up” (selection of the best) phase. Each phase provides a probability 1 convergence guarantee as the simulation effort increases without bound: Convergence to a globally optimal solution in the global-search phase; convergence to a locally optimal solution in the local-search phase; and convergence to the best of a small number of good solutions in the clean-up phase. In practice, ISC stops short of such convergence by applying an improvement-based transition rule from the global phase to the local phase; a statistical test of convergence from the local phase to the clean-up phase; and a ranking-and-selection procedure to terminate the clean-up phase. Small-sample validity of the statistical test and ranking-and-selection procedure is proven for normally distributed data. ISC is compared to the commercial optimization via simulation package OptQuest on five test problems that range from 2 to 20 decision variables and on the order of 104 to 1020 feasible solutions. These test cases represent response-surface models with known properties and realistic system simulation problems.