Robust parameter design optimization of simulation experiments using stochastic perturbation methods

Journal of the Operational Research Society

Volumn 62, Issue 1, 2011, Pages 198-205

  Miranda, A K ^a   Del Castillo, E ^a  

^a The Center for Nanotechnology Education and Utilization   (United States)

Author keywords

crossed arrays; noise factors; non homogeneous variance; simulation optimization

Indexed keywords

DISCRETE EVENT SIMULATION; DISTRIBUTED COMPUTER SYSTEMS; ITERATIVE METHODS; OPTIMIZATION; STOCHASTIC SYSTEMS;

CROSSED ARRAY; NOISE FACTOR; NON-HOMOGENEOUS; OBJECTIVE FUNCTIONS; ROBUST PARAMETER DESIGN; SIMULATION OPTIMIZATION; SIMULTANEOUS PERTURBATION STOCHASTIC APPROXIMATION METHODS; STOCHASTIC PERTURBATION METHOD;

PERTURBATION TECHNIQUES;

EID: 78649925761     PISSN: 01605682     EISSN: 14769360     Source Type: Journal    
DOI: 10.1057/jors.2009.163     Document Type: Article

References (29)

1. Benjamin P and Erraguntla M (1995). Using simulation for robust system design. Simulation 65:116-128.
2. Beyer H and Sendhoff B (2007). Robust optimization-A comprehensive survey. Comput Method Appl M 196:3190-3218.
3. Chin D (1997). Comparative study of stochastic algorithms for system optimization based on gradient approximations. IEEE T Syst Man CYB 27, pp 244-249.
4. Cupertino F, Mininno E, Naso D and Turchiano B (2006). An experimental implementation of SPSA algorithms for induction motor adaptive control. IEEE Mountain Workshop on Adaptive and Learning Systems.
5. Del Castillo E (2007). Process Optimization: A Statistical Approach. Springer: New York, NY.
6. Elsayed EA and Boucher TO (1985). Analysis and Control of Production Systems. Prentice-Hall: Englewood Cliffs, NJ.
7. Fu M and Hill S (1997). Optimization of discrete event systems via simultaneous perturbation stochastic approximation. IEEE Trans 29:233-243.
8. Gerencsér L (1998). SPSA with state-dependent noise-A tool for direct adaptive control. In: Proceedings of the 37th IEEE Conference on Decision and Control, Vol. 3, Tampa, FL, pp 3451-3456.
9. Hill S, Gerencsér L and Vágó Z (2004). Stochastic approximation on discrete sets using simultaneous difference approximations. In: Proceedings of the 2004 American Control Conference, Vol. 3, Laurel, MD, pp 2795-2798.
10. Ho YC, Cassandras CG, Chen CH and Dai L (2000). Ordinal optimisation and simulation. J Opl Res Soc 51:490-500.
11. Hurrion R (1997). An example of simulation optimisation using a neural network metamodel: Finding the optimum number of kanbans in a manufacturing system. J Opl Res Soc 48:1105-1112.
12. Hutchison D (2002). On an efficient distribution of perturbations for simulation optimization using simultaneous perturbation stochastic approximation. In: Proceedings of the IASTED International Conference Applied Modeling and Simulation, Cambridge, MA, pp 440-445.
13. Ittiwattana W (2002). A method for simulation optimization with applications in robust process design and locating supply chain operations. PhD dissertation. The Ohio State University.
14. Kleijnen J, Bettonvil B and Persson J (2003). Robust solutions for supply chain management: Simulation, optimization, and risk analysis, http://center.uvt.nl/staff/kleijnen.
15. Kocsis L and Szepesvári C (2006). Universal parameter optimisation in games based on SPSA. Mach Learn 63:249-286.
16. Mayer RJ and Benjamin P (1992). Using the taguchi paradigm for manufacturing system design using simulation experiments. Comput Ind Eng 22:195-209.
17. Miranda AK (2008). Stochastic perturbation methods for robust optimization of simulation experiments. MS thesis. The Pennsylvania State University.
18. Moré J, Garbow B and Hillstrom K (1981). Testing unconstrained optimization software. ACM T Math Software 7:17-41.
19. Myers RH and Montgomery DC (2002). Response Surface Methodology Process and Product Optimization Using Designed Experiments. Wiley-Interscience: New York, NY.
20. Rezayat F (1994). On the use of SPSA-based model-free controller in quality improvement. In: Proceedings of the 33rd Conference on Decision and Control, Vol. 2, Lake Buena Vista, FL, pp 1876-1878.
21. Sadegh P (1997). Constrained optimization via stochastic approximation with a simultaneous perturbation gradient approximation. Automatica 33:889-892.
22. Schwartz J, Wang W and Rivera D (2006). Simulation-based optimization of process control policies for inventory management in supply chains. Automatica 42:1311-1320.
23. Sohn SY (2002). Robust design of server capability in M/M/1 queues with both partly random arrival and service rates. Comput Opns Res 29:433-440.
24. Spall J (1992). Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE T Automat Contr 37:332-341.
25. Spall J (2003). Introduction to Stochastic Search and Optimization. Wiley-Interscience: New York, NY.
26. Spall J and Cristion J (1997). A neural network controller for systems with unmodeled dynamics with applications to wastewater treatment. IEEE T Syst Man CYB 27, pp 369-375.
27. Wang L (2006). Augumented simultaneous perturbation stochastic approximation (Aspsa) for discrete supply chain inventory optimization problems. PhD dissertation. The Pennsylvania State University.
28. Wild R and Pignatiello J (1991). An experimental design strategy for designing robust systems using discrete-event simulation. Simulation 57:358-368.
29. Yeomans J (2002). Automatic generation of efficient policy alternatives via simulation-optimization. J Opl Res Soc 53:1256-1267.