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仿真优化:理论与应用综述

王龙飞 侍乐媛

王龙飞, 侍乐媛. 仿真优化:理论与应用综述. 自动化学报, 2013, 39(11): 1957-1968. doi: 10.3724/SP.J.1004.2013.01957
引用本文: 王龙飞, 侍乐媛. 仿真优化:理论与应用综述. 自动化学报, 2013, 39(11): 1957-1968. doi: 10.3724/SP.J.1004.2013.01957
WANG Long-Fei, SHI Le-Yuan. Simulation Optimization: A Review on Theory and Applications. ACTA AUTOMATICA SINICA, 2013, 39(11): 1957-1968. doi: 10.3724/SP.J.1004.2013.01957
Citation: WANG Long-Fei, SHI Le-Yuan. Simulation Optimization: A Review on Theory and Applications. ACTA AUTOMATICA SINICA, 2013, 39(11): 1957-1968. doi: 10.3724/SP.J.1004.2013.01957

仿真优化:理论与应用综述

doi: 10.3724/SP.J.1004.2013.01957
基金项目: 

Supported by National High Technology Research and Development Program of China (863 Program) (2012AA040909)

Simulation Optimization: A Review on Theory and Applications

Funds: 

Supported by National High Technology Research and Development Program of China (863 Program) (2012AA040909)

More Information
    Corresponding author: SHI Le-Yuan
  • 摘要: 对于现实中的复杂系统, 仿真优化是一种非常强大的分析和优化工具. 本文对仿真优化领域的相关理论与方法进行了介绍与回顾. 根据系统中决策变量的性质的不同(连续或者离散变量), 我们对仿真优化问题进行了分类. 而且我们对仿真优化领域中的重要技术进行了详细地讨论, 包括它们的原理、使用方法、优势和劣势以及应用等. 关于仿真优化领域未来的研究方向, 我们也进行了相关论述.
  • [1] Tekin E, Sabuncuoglu I. Simulation optimization: a comprehensive review on theory and applications. IIE Transactions, 2004, 36(11): 1067-1081
    [2] Meketon M S. Optimization in simulation: a survey of recent results. In: Proceedings of the 1987 Winter Simulation Conference. Piscataway, NJ: IEEE, 1987. 58-67
    [3] Jacobson S H, Schruben L W. Techniques for simulation response optimization. Operations Research Letters, 1989, 8(1): 1-9
    [4] Azadivar F. A tutorial on simulation optimization. In: Proceedings of the 1992 Winter Simulation Conference. Piscataway, NJ: IEEE, 1992. 198-204
    [5] Fu M C. Optimization via simulation: a review. Annals of Operations Research, 1994, 53(1): 199-247
    [6] Carson Y, Maria A. Simulation optimization: methods and applications. In: Proceedings of the 1997 Winter Simulation Conference. Atlanta, Georgia: IEEE, 1997. 118-126
    [7] Andradóttir S. A review of simulation optimization techniques. In: Proceedings of the 1998 Winter Simulation Conference. Piscataway, NJ: IEEE, 1998. 151-158
    [8] Bowden R O, Hall J D. Simulation optimization research and development. In: Proceedings of the 1998 Winter Simulation Conference. Washington, DC: IEEE, 1998. 1693-1698
    [9] Azadivar F. Simulation optimization methodologies. In: Proceedings of the 1999 Winter Simulation Conference. Phoenix, AZ: IEEE, 1999. 93-100
    [10] Swisher J R, Hyden P D, Jacobson S H, Schruben L W. A survey of simulation optimization techniques and procedures. In: Proceedings of the 2000 Winter Simulation Conference. Orlando, FL: IEEE, 2000. 119-128
    [11] Law A M, McComas M G. Simulation-based optimization. In: Proceedings of the 2000 Winter Simulation Conference. San Diego, CA, USA: IEEE, 2000. 46-49
    [12] Fu M C. Simulation optimization. In: Proceedings of the 2001 Winter Simulation Conference. Arlington, VA, USA: IEEE, 2001. 53-61
    [13] Fu M C. Optimization for simulation: theory vs. practice. INFORMS Journal on Computing, 2002, 14(3): 192-215
    [14] Ólafsson S, Kim J. Simulation optimization. In: Proceedings of the 2002 Winter Simulation Conference. Piscataway, NJ: IEEE, 2002. 79-84
    [15] Law A M, McComas M G. Simulation optimization: simulation-based optimization. In: Proceedings of the 2002 Winter Simulation Conference. San Diego, CA, USA: IEEE, 2002. 41-44
    [16] April J, Glover F, Kelly J P, Laguna M. Practical introduction to simulation optimization. In: Proceedings of the 2003 Winter Simulation Conference. New Orleans, LA, USA: IEEE, 2003. 71-78
    [17] Fu M C, Glover F W, April J. Simulation optimization: a review, new developments, and applications. In: Proceedings of the 2005 Winter Simulation Conference. Orlando, FL: IEEE, 2005. 83-95
    [18] Fu M C, Chen C H, Shi L. Some topics for simulation optimization. In: Proceedings of the 2008 Winter Simulation Conference. Austin, TX: IEEE, 2008. 27-38
    [19] Ho Y C, Eyler M A, Chien T T. A gradient technique for general buffer storage design in a production line. International Journal of Production Research, 1979, 17(6): 557-580
    [20] Ho Y C, Cao X R. Perturbation Analysis of Discrete Event Dynamic Systems. Norwell, MA, USA: Kluwer Academic Pub, 1991
    [21] Heidelberger P. Limitations of Infinitesimal Perturbation Analysis. International Business Machine Research Report RC 11891, IBM Watson Research Labs, Yorktown Heights, NY, 1986
    [22] Glasserman P. Structural conditions for perturbation analysis derivative estimation: finite-time performance indices. Operations Research, 1991, 39(5): 724-738
    [23] L'Ecuyer P, Perron G. On the convergence rates of IPA and FDC derivative estimators. Operations Research, 1994, 42(4): 643-656
    [24] Dai L L. Perturbation analysis via coupling. IEEE Transactions on Automatic Control, 2000, 45(4): 614-628
    [25] Ho Y C, Suri R, Cao X R, Diehl G W, Dille J W, Zazanis M. Optimization of large multiclass (non-product-form) queueing networks using perturbation analysis. Large Scale Systems, 1984, 7: 165-180
    [26] Ho Y C. On the perturbation analysis of discrete-event dynamic systems. Journal of Optimization Theory and Applications, 1985, 46(4): 535-545
    [27] Ho Y C, Hu J Q. An infinitesimal perturbation analysis algorithm for a multiclass G/G/1 queue. Operations Research Letters, 1990, 9(1): 35-44
    [28] Chong E K P, Ramadge P J. Optimization of queues using an infinitesimal perturbation analysis-based stochastic algorithm with general update times. SIAM Journal on Control and Optimization, 1993, 31(3): 698-732
    [29] Fu M C, Hu J Q. Smoothed perturbation analysis derivative estimation for Markov chains. Operations Research Letters, 1994, 15(5): 241-251
    [30] Fu M C. Sample path derivatives for (s, S) inventory systems. Operations Research, 1994, 42(2): 351-364
    [31] Bashyam S, Fu M C. Application of perturbation analysis to a class of periodic review (s, S) inventory systems. Naval Research Logistics (NRL), 1994, 41(1): 47-80
    [32] Bashyam S, Fu M C. Optimization of (s, S) inventory systems with random lead times and a service level constraint. Management Science, 1998, 44(12-Part-2): S243-S256
    [33] Donohue K L, Spearman M L. Improving the design of stochastic production lines: an approach using perturbation analysis. International Journal of Production Research, 1993, 31(12): 2789-2806
    [34] Yan H, Yin G, Lou S X C. Using stochastic optimization to determine threshold values for the control of unreliable manufacturing systems. Journal of Optimization Theory and Applications, 1994, 83(3): 511-539
    [35] Liberopoulos G, Caramanis M. Infinitesimal perturbation analysis for second derivative estimation and design of manufacturing flow controllers. Journal of Optimization Theory and Applications, 1994, 81(2): 297-327
    [36] Cheng D W. On the design of a tandem queue with blocking: modeling, analysis, and gradient estimation. Naval Research Logistics (NRL), 1994, 41(6): 759-770
    [37] Heidergott B. Sensitivity analysis of a manufacturing workstation using perturbation analysis techniques. International Journal of Production Research, 1995, 33(3): 611-622
    [38] Brooks C A, Varaiya P. Using augmented infinitesimal perturbation analysis for capacity planning in intree ATM networks. Discrete Event Dynamic Systems, 1997, 7(4): 377-390
    [39] Heidergott B. Optimisation of a single-component maintenance system: a smoothed perturbation analysis approach. European Journal of Operational Research, 1999, 119(1): 181-190
    [40] Schruben L W, Cogliano V J. Simulation sensitivity analysis: a frequency domain approach. In: Proceedings of the 1981 Winter Simulation Conference. Piscataway, NJ: IEEE, 1981. 455-459
    [41] Jacobson S H, Morrice D, Schruben L W. The global simulation clock as the frequency domain experiment index. In: Proceedings of the 1988 Winter Simulation Conference, San Diego, CA, USA: IEEE, 1988. 558-563
    [42] Jacobson S H, Buss A H, Schruben L W. Driving frequency selection for frequency domain simulation experiments. Operations Research, 1991, 39(6): 917-924
    [43] Hazra M M, Morrice D J, Park S K. A simulation clock-based solution to the frequency domain experiment indexing problem. IIE Transactions, 1997, 29(9): 769-782
    [44] Glynn P W. Likelihood ratio gradient estimation: an overview. In: Proceedings of the 1987 Winter Simulation Conference. Piscataway, NJ: IEEE, 1987. 366-375
    [45] Glynn P W. Likelihood ratio gradient estimation for stochastic systems. Communications of the ACM, 1990, 33(10): 75 -84
    [46] Rubinstein R Y. Sensitivity analysis and performance extrapolation for computer simulation models. Operations Research, 1989, 37(1): 72-81
    [47] Rubinstein R Y. Optimization of computer simulation models with rare events. European Journal of Operational Research, 1997, 99(1): 89-112
    [48] Rubinstein R Y, Shapiro A. Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method. New York: Wiley, 1993
    [49] Nakayama M K, Goyal A, Glynn P W. Likelihood ratio sensitivity analysis for Markovian models of highly dependable systems. Operations Research, 1994, 42(1): 137-157
    [50] Nakayama M K, Shahabuddin P. Likelihood ratio derivative estimation for finite-time performance measures in generalized semi-Markov processes. Management Science, 1998, 44(10): 1426-1441
    [51] Andradóttir, S. Optimization of the transient and steady-state behavior of discrete event systems. Management Science, 1996, 42(5): 717-737
    [52] Fu M C, Hu J Q, Nagi R. Comparison of gradient estimation techniques for queues with non-identical servers. Computers and Operations Research, 1995, 22(7): 715-729
    [53] Fu M C, Hu J Q. Efficient design and sensitivity analysis of control charts using Monte Carlo simulation. Management Science, 1999, 45(3): 395-413
    [54] Fu M C. 2006. Gradient estimation. Handbooks in Operations Research and Management Science: Simulation, Chapter 19 (Henderson S G, Nelson B L (editor)). Amsterdam: Elsevier. 575-616
    [55] Fu M C. What you should know about simulation and derivatives. Naval Research Logistics (NRL), 2008, 55(8): 723-736
    [56] Robbins H, Monro S. A stochastic approximation method. The Annals of Mathematical Statistics, 1951, 22(3): 400-407
    [57] Kiefer J, Wolfowitz J. Stochastic estimation of the maximum of a regression function. The Annals of Mathematical Statistics, 1952, 23(3): 462-466
    [58] Glynn P W. Stochastic approximation for Monte Carlo optimization. In: Proceedings of the 1986 Winter Simulation Conference. Piscataway, NJ: IEEE, 1986. 356-365
    [59] Kouritzin M A. On the convergence of linear stochastic approximation procedures. IEEE Transactions on Information Theory, 1996, 42(4): 1305-1309
    [60] Kulkarni S R, Horn C S. An alternative proof for convergence of stochastic approximation algorithms. IEEE Transactions on Automatic Control, 1996, 41(3): 419-424
    [61] L'Ecuyer P, Yin G. Budget-dependent convergence rate of stochastic approximation. SIAM Journal on Optimization, 1998, 8(1): 217-247
    [62] Andradóttir S. A stochastic approximation algorithm with varying bounds. Operations Research, 1995, 43(6): 1037-1048
    [63] Kleinman N L, Spall J C, Naiman D Q. Simulation-based optimization with stochastic approximation using common random numbers. Management Science, 1999, 45(11): 1570-1578
    [64] Fu M C, Ho Y C. Using perturbation analysis for gradient estimation, averaging and updating in a stochastic approximation algorithm. In: Proceedings of the 1988 Winter Simulation Conference. San Diego, CA, USA, 1988. 509-517
    [65] Fu M C. Convergence of a stochastic approximation algorithm for the GI/G/1 queue using infinitesimal perturbation analysis. Journal of Optimization Theory and Applications, 1990, 65(1): 149-160
    [66] L'Ecuyer P, Glynn P W. Stochastic optimization by simulation: convergence proofs for the GI/G/1 queue in steady-state. Management Science, 1994, 40(11): 1562-1578
    [67] Hill S D, Fu M C. Optimizing discrete event systems with the simultaneous perturbation stochastic approximation algorithm. In: Proceedings of the 33rd IEEE Conference on Decision and Control. Lake Buena Vista, FL: IEEE, 1994. 2631-2632
    [68] Fu M C, Hill S D. Optimization of discrete event systems via simultaneous perturbation stochastic approximation. IIE Transactions, 1997, 29(3): 233-243
    [69] Fu M, Hu J Q. Conditional Monte Carlo: Gradient Estimation and Optimization Applications. Boston: Kluwer Academic Publishers, 1997
    [70] Chong E K P, Ramadge P J. Optimal load sharing in soft real-time systems using likelihood ratios. Journal of Optimization Theory and Applications, 1994, 82(1): 23-48
    [71] Andradöttir S. A scaled stochastic approximation algorithm. Management Science, 1996, 42(4): 475-498
    [72] Tang Q Y, Chen H F, Han Z J. Convergence rates of Perturbation-Analysis-Robbins-Monro-Single-Run algorithms for single server queues. IEEE Transactions on Automatic Control, 1997, 42(10): 1442-1447
    [73] Tang Q Y, L'Ecuyer P, Chen H F. Asymptotic efficiency of perturbation-analysis-based stochastic approximation with averaging. SIAM Journal on Control and Optimization, 1999, 37(6): 1822-1847
    [74] Tang Q Y, Chen H F. Central limit theorems for stochastic optimization algorithms using infinitesimal perturbation analysis. Discrete Event Dynamic Systems, 2000, 10(1-2): 5-32
    [75] Hasan C N, Spearman M L. Optimal material release times in stochastic production environments. International Journal of Production Research, 1999, 37(6): 1201-1216
    [76] Andradóttir S. A new algorithm for stochastic optimization. In: Proceedings of the 1990 Winter Simulation Conference. New Orleans, LA: IEEE, 1986. 364-366
    [77] Andradóttir S. A projected stochastic approximation algorithm. In: Proceedings of the 1991 Winter Simulation Conference. Phoenix, AZ: IEEE, 1991. 954-957
    [78] Yakowitz S. A globally convergent stochastic approximation. SIAM Journal on Control and Optimization, 1993, 31(1): 30 -40
    [79] Kushner H J, Yin G. Stochastic Approximation and Recursive Algorithms and Applications (2nd edition). New York: Springer-Verlag, 2003
    [80] Gurkan G, Yonca-Ozge A, Robinson T M. Sample-path optimization in simulation. In: Proceedings of the 1994 Winter Simulation Conference. Lake Buena Vista, FL, USA: IEEE, 1994. 247-254
    [81] Homem-de-Mello T, Shapiro A, Spearman M L. Finding optimal material release times using simulation-based optimization. Management Science, 1999, 45(1): 86-102
    [82] Kleywegt A J, Shapiro A, Homem-de-Mello T. The sample average approximation method for stochastic discrete optimization. SIAM Journal on Optimization, 2002, 12(2): 479-502
    [83] Chen H F, Schmeiser B W. Retrospective approximation algorithms for stochastic root finding. In: Proceedings of the 1994 Winter Simulation Conference. Lake Buena Vista, FL, USA: IEEE, 1994. 255-261
    [84] Shapiro A. Simulation based optimization. In: Proceedings of the 1996 Winter Simulation Conference. Washington, DC: IEEE, 1996. 332-336
    [85] Box G E P, Wilson K B. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B (Methodological), 1951, 13(1): 1-45
    [86] Box G E P. The exploration and exploitation of response surfaces: some general considerations and examples. Biometrics, 1954, 10(1): 16-60
    [87] Biles W E. A gradient-regression search procedure for simulation experimentation. In: Proceedings of the 1974 Winter Simulation Conference. Piscataway, NJ: IEEE, 1974. 491-497
    [88] Smith D E. Automatic optimum-seeking program for digital simulation. Simulation, 1976, 27(1): 27-31
    [89] Daughety A F, Turnquist M A. Simulation optimization using response surfaces based on spline approximations. In: Proceedings of the 1978 Winter Simulation Conference. Piscataway, NJ: IEEE, 1978. 183-193
    [90] Wilson J R. Future directions in response surface methodology for simulation. In: Proceedings of the 1987 Winter Simulation Conference. Piscataway, NJ: IEEE, 1987. 378-381
    [91] Myers R H, Khuri A I, Carter W H Jr. Response surface methodology: 1966-1988. Technometrics, 1989, 31(2): 137-157
    [92] Safizadeh M H, Signorile R. Optimization of simulation via quasi-Newton methods. ORSA Journal on Computing, 1994, 6(4): 398-408
    [93] Joshi S, Sherali H D, Tew J D. An enhanced response surface methodology (RSM) algorithm using gradient deflection and second-order search strategies. Computers and Operations Research, 1998, 25(7-8): 531-541
    [94] Kleijnen J P C. 1998. Experimental design for sensitivity analysis, optimization, and validation of simulation models. Handbook of Simulation, Chapter 6 (Banks J (editor)). New York: Wiley. 173-223
    [95] Kleijnen J P C. Simulation and optimization in production planning: a case study. Decision Support Systems, 1993, 9(3): 269-280
    [96] Kleijnen J P C. Sensitivity analysis and optimization of system dynamics models: regression analysis and statistical design of experiments. System Dynamics Review, 1995, 11(4): 275-288
    [97] Shang J S, Tadikamalla P R. Output maximization of a CIM system: simulation and statistical approach. International Journal of Production Research, 1993, 31(1): 19-41
    [98] Barton R R, Meckesheimer M. 2006. Metamodelbased simulation optimization. Handbooks in Operations Research and Management Science: Simulation, Chapter 18 (Henderson S G, Nelson B L (editor)). Amsterdam: Elsevier. 535-574
    [99] Kleijnen J P C. Design and Analysis of Simulation Experiments. New York: Springer, 2007
    [100] Dudewicz E J, Dalal S R. Allocation of observations in ranking and selection with unequal variances. Sankhyã: The Indian Journal of Statistics, Series B, 1975, 37(1): 28-78
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  • 收稿日期:  2013-07-01
  • 修回日期:  2013-08-29
  • 刊出日期:  2013-11-20

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