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Optimization via simulation: A review
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Citations
Cites
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Simulation Modeling and Analysis.
Schervish, M. J., Law, A. M., & Kelton, W. D. (1983) · Journal of the American Statistical Association · doi:10.2307/2288169 · Cited by 8,697
Empirical Model Building and Response Surfaces
Freeny, A. (1988) · Technometrics · doi:10.1080/00401706.1988.10488371 · Cited by 5,235
Myers, R. H., & Montgomery, D. C. (1996) · IIE Transactions · doi:10.1080/15458830.1996.11770760 · Cited by 3,819
Discrete-Event System Simulation
Morgan, C. B. (1984) · Technometrics · doi:10.1080/00401706.1984.10487955 · Cited by 3,593
Multiple Comparison Procedures
Hochberg, Y., & Tamhane, A. C. (1987) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316672 · Cited by 2,389
Stochastic Estimation of the Maximum of a Regression Function
Kiefer, J., & Wolfowitz, J. (1952) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729392 · Cited by 2,133
Multiple Comparison Procedures
(2013) · An Introduction to Statistical Concepts · doi:10.4324/9780203137819-17 · Cited by 2,109
Adaptive Algorithms and Stochastic Approximations
Benveniste, A., Métivier, M., & Priouret, P. (1990) · Springer eBooks · doi:10.1007/978-3-642-75894-2 · Cited by 2,006
Empirical Model-Building and Response Surfaces.
Kshirsagar, A. M., Box, G. E. P., & Draper, N. R. (1988) · Journal of the American Statistical Association · doi:10.2307/2288890 · Cited by 1,894
Cochran, W. G., & Cox, G. M. (1957) · Cited by 1,729
Analysis of recursive stochastic algorithms
Ljung, L. (1977) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1977.1101561 · Cited by 1,467
Stochastic Approximation Methods for Constrained and Unconstrained Systems
Kushner, H. J., & Clark, D. S. (1978) · Applied Mathematical Sciences · doi:10.1007/978-1-4684-9352-8 · Cited by 1,459
Bechhofer, R. E. (1954) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728845 · Cited by 928
Gradient Estimation via Perturbation Analysis
(1991) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(91)90124-l · Cited by 707
(2005) · Interdisciplinary Mathematical Sciences · doi:10.1142/9789812701039_0003 · Cited by 611
Response Surface Methodology: 1966-1988
Myers, R. H., Khuri, A. I., Carter, W. H., & Khuri, A. I. (1989) · Technometrics · doi:10.2307/1268813 · Cited by 594
Optimization of stochastic systems
Glynn, P. W. (1986) · Proceedings of the 18th conference on Winter simulation - WSC '86 · doi:10.1145/318242.318260 · Cited by 573
Statistical tools for simulation practitioners
(1990) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(90)90147-b · Cited by 528
Stochastic Approximation and Recursive Estimation
Nevel′son, M., & Has′minskiĭ, R. (1976) · Translations of Mathematical Monographs · doi:10.1090/mmono/047 · Cited by 525
Manson, A. R. (1973) · Technometrics · doi:10.1080/00401706.1973.10489128 · Cited by 466
Accelerated Stochastic Approximation
Kesten, H. (1958) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706705 · Cited by 433
Goodfriend, J. H., Rubinstein, R. Y., & Shapiro, A. (1995) · Technometrics · doi:10.1080/00401706.1995.10485910 · Cited by 383
Likelihood ratio gradient estimation for stochastic systems
Glynn, P. W. (1990) · Communications of the ACM · doi:10.1145/84537.84552 · Cited by 381
Asymptotic Distribution of Stochastic Approximation Procedures
Sacks, J. (1958) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177706619 · Cited by 368
On a Stochastic Approximation Method
Chung, K. L. (1954) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177728716 · Cited by 363
Statistical Tools for Simulation Practitioners
Thode, H. C. (1988) · Technometrics · doi:10.1080/00401706.1988.10488459 · Cited by 227
Perturbation Analysis Gives Strongly Consistent Sensitivity Estimates for the M/G/1 Queue
Suri, R., & Zazanis, M. A. (1988) · Management Science · doi:10.1287/mnsc.34.1.39 · Cited by 226
Likelilood ratio gradient estimation: an overview
Glynn, P. W. (1987) · Proceedings of the 19th conference on Winter simulation - WSC '87 · doi:10.1145/318371.318612 · Cited by 219
Perturbation analysis: the state of the art and research issues explained via the GI/G/1 queue
Suri, R. (1989) · Proceedings of the IEEE · doi:10.1109/5.21075 · Cited by 211
Sensitivity Analysis and Performance Extrapolation for Computer Simulation Models
Rubinstein, R. Y. (1989) · Operations Research · doi:10.1287/opre.37.1.72 · Cited by 179
Very Large-Scale Linear Programming: A Case Study in Combining Interior Point and Simplex Methods
Bixby, R. E., Gregory, J. W., Lustig, I. J., Marsten, R. E., & Shanno, D. F. (1992) · Operations Research · doi:10.1287/opre.40.5.885 · Cited by 164
Design of Experiments: Ranking and Selection
Santner, T. J., & Tamhane, A. C. (1984) · Cited by 157
Techniques for simulation response optimization
Jacobson, S. H., & Schruben, L. W. (1989) · Operations Research Letters · doi:10.1016/0167-6377(89)90025-4 · Cited by 116
Experimental Designs for Computer Simulation Experiments
Hunter, J. S., & Naylor, T. H. (1970) · Management Science · doi:10.1287/mnsc.16.7.422 · Cited by 99
Stochastic Optimization by Simulation: Convergence Proofs for the GI/G/1 Queue in Steady-State
L’Ecuyer, P., & Glynn, P. W. (1994) · Management Science · doi:10.1287/mnsc.40.11.1562 · Cited by 98
Strong Convergence of a Stochastic Approximation Algorithm
Lennart Ljung, A. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344212 · Cited by 97
Optimization in simulation: Current issues and the future outlook
Safizadeh, M. H. (1990) · Naval Research Logistics (NRL) · doi:10.1002/1520-6750(199012)37:6 · Cited by 86
Single Run Optimization of Discrete Event Simulations—An Empirical Study Using the M/M/l Queue
Suri, R., & Leung, Y. T. (1989) · IIE Transactions · doi:10.1080/07408178908966205 · Cited by 85
Methods for selecting the best system (for simulation)
Goldsman, D. M., Nelson, B. L., & Schmeiser, B. W. (2002) · Winter Simulation Conference · doi:10.1109/wsc.1991.185613 · Cited by 80
On-line sensitivity analysis of Markov chains
Cassandras, C. G., & Strickland, S. G. (1989) · IEEE Transactions on Automatic Control · doi:10.1109/9.8651 · Cited by 80
Chong, E. K. P., & Ramadge, P. J. (1993) · SIAM Journal on Control and Optimization · doi:10.1137/0331032 · Cited by 78
Stochastic approximation method with gradient averaging for unconstrained problems
Ruszczyński, A., & Syski, W. (1983) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1983.1103184 · Cited by 62
Optimization of stochastic simulation models
Azadivar, F., & Talavage, J. J. (1980) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(80)90050-6 · Cited by 61
Monte Carlo Optimization, Simulation and Sensitivity of Queueing Networks
Angus, J. E., & Rubinstein, R. Y. (1988) · Technometrics · doi:10.2307/1269828 · Cited by 42
Research issues in metamodeling
Sargent, R. G. (1991) · NCSU Libraries Repository (North Carolina State University Libraries) · doi:10.5555/304238.304365 · Cited by 26
Optimization and Industrial Experimentation
Dawkins, D. A., Biles, W. J., & Swain, J. J. (1981) · Journal of the Operational Research Society · doi:10.2307/2581266 · Cited by 20
Stochastic Approximation Via Averaging: The Polyak’s Approach Revisited
Yin, G. (1992) · Lecture notes in economics and mathematical systems · doi:10.1007/978-3-642-48914-3_9 · Cited by 11
Cited by
Monte Carlo sampling-based methods for stochastic optimization
Homem-de-Mello, T., & Bayraksan, G. (2014) · Surveys in Operations Research and Management Science · doi:10.1016/j.sorms.2014.05.001 · Cited by 283
Chapter 18 Metamodel-Based Simulation Optimization
Barton, R. R., & Meckesheimer, M. (2006) · Handbooks in Operations Research and Management Science · doi:10.1016/s0927-0507(06)13018-2 · Cited by 265
Pharmacoeconomic Analyses Using Discrete Event Simulation
Caro, J. J. (2005) · PharmacoEconomics · doi:10.2165/00019053-200523040-00003 · Cited by 191
Metadata
- Authors
- Fu, M. C.
- Venue
- Annals of Operations Research
- Published
- 1994-12
- Volume
- 53
- Issue
- 1
- Pages
- 199-247
- ISSN
- 0254-5330 (ISSN-L)
- 1572-9338
- OpenAlex
- W2108353709
- Cited by
- 417
- References
- 48
- FWCI
- 16.91
- Type
- journal article
- Language
- en
- Open access
- closed
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
