Efficient Simulation Budget Allocation for Selecting an Optimal Subset
Published Online:30 May 2008https://doi.org/10.1287/ijoc.1080.0268
References
- Analysis methodology: Are we done? Proc. 2005 Winter Simulation Conf. (2005) (Association for Computing Machinery, New York) 790–796Crossref, Google Scholar
- Population-based incremental learning: A method for integrating genetic search based function optimization and competitive learning. (1994) . Technical Report CMU-CS-94-163, School of Computer Science, Carnegie Mellon University, PittsburghGoogle Scholar
- Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons (1995) (John Wiley & Sons, New York) Google Scholar
- Selecting a selection procedure. Management Sci. (2007) 53:1916–1932Link, Google Scholar
- Practical Handbook of Genetic Algorithms (1995) (CRC Press, Boca Raton, FL) Crossref, Google Scholar
- A lower bound for the correct subset-selection probability and its application to discrete event system simulations. IEEE Trans. Automatic Control (1996) 41:1227–1231Crossref, Google Scholar
- Efficient dynamic simulation allocation in ordinal optimization. IEEE Trans. Automatic Control (2006) 51:2005–2009Crossref, Google Scholar
- Simulation budget allocation for further enhancing the efficiency of ordinal optimization. J. Discrete Event Dynam. Systems: Theory Appl. (2000) 10:251–270Crossref, Google Scholar
- New development of optimal computing budget allocation for discrete event simulation. Proc. 1997 Winter Simulation Conf. (1997) (Association for Computing Machinery, New York) 334–341Crossref, Google Scholar
- New two-stage and sequential procedures for selecting the best simulated system. Oper. Res. (2001a) 49:1609–1624Link, Google Scholar
- New procedures to select the best simulated system using common random numbers. Management Sci. (2001b) 47:1133–1149Link, Google Scholar
- Optimal Statistical Decisions (1970) (McGraw-Hill, New York) Google Scholar
- Allocation of observations in ranking and selection with unequal variances. Sankhya (1975) 37:28–78Google Scholar
- Model-based randomized methods for global optimization. Proc. 17th Internat. Sympos. Math. Theory Networks Systems, Kyoto, Japan (2006) 355–363Google Scholar
- Simulation allocation for determining the best design in the presence of correlated sampling. INFORMS J. Comput. (2007) 19:101–111Link, Google Scholar
- , Banks J. Comparing systems via simulation. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice (1998) (John Wiley & Sons, New York) 273–306Crossref, Google Scholar
- On some multiple decision (selection and ranking) rules. Technometrics (1965) 7:225–245Crossref, Google Scholar
- Opportunity cost and OCBA selection procedures in ordinal optimization for a fixed number of alternative systems. IEEE Trans. Systems, Man, Cybernetics—Part C (2007) 37:951–961Crossref, Google Scholar
- Adaptation in Natural and Artificial Systems (1975) (The University of Michigan Press, Ann Arbor) Google Scholar
- A model reference adaptive search algorithm for global optimization. Oper. Res. (2007a) 55:549–568Link, Google Scholar
- A model reference adaptive search algorithm for stochastic optimization with applications to Markov decision processes. Proc. 46th IEEE Conf. Decision Control (2007b) (IEEE Press, Washington, D.C.) 975–980Google Scholar
- Comparison of Bayesian and frequentist assessments of uncertainty for selecting the best system. Proc. 1998 Winter Simulation Conf. (1998) (Institute of Electrical and Electronic Engineers, Piscataway, NJ) 727–734Crossref, Google Scholar
- An empirical evaluation of several methods to select the best system. ACM Transitions Model. Comput. Simulation (1999) 9:381–407Crossref, Google Scholar
- , Henderson S. G., Nelson B. L. Selecting the best system. Handbooks in Operations Research and Management Science: Simulation (2006) (Elsevier, New York) . Chapter 18Google Scholar
- A procedure for selecting a subset of size m containing the l best of k independent normal populations. Comm. Statist.—Simulation Comm. (1985) 14:719–734Crossref, Google Scholar
- Simulation Modeling Analysis (2000) (McGraw-Hill, New York) Google Scholar
- Optimal computing budget allocation for multi-objective simulation models. Proc. 2004 Winter Simulation Conf. (2004) (Association for Computing Machinery, New York) 586–594Crossref, Google Scholar
- On two-stage selection procedures and related probability inequalities. Comm. Statist. (1978) 7:799–811Crossref, Google Scholar
- The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation, and Machine Learning (2004) (Springer, New York) Crossref, Google Scholar
- Stochastic hill climbing with learning by vectors of normal distributions. Proc. First Online Workshop on Soft Comput. (1996) (Nagoya University, Nagoya, Japan) 60–70Google Scholar
- Restricted subset selection procedures for simulation. Oper. Res. (1989) 37:52–71Link, Google Scholar
- Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures: A survey. ACM Trans. Model. Comput. Simulation (2003) 13:134–154Crossref, Google Scholar
- Computing budget allocation for efficient ranking and selection of variances with application to target tracking algorithms. IEEE Trans. Automatic Control (2004) 49:58–67Crossref, Google Scholar
- Introduction to Mathematical Programming (1999) (Prentice Hall, Upper Saddle River, NJ) Google Scholar

