Erratum: Response surface methodology's steepest ascent and step size revisited (European Journal of Operational Research 159 (121-131) DOI:10.1016/S0377-2217(03)00414-4);Response surface methodology's steepest ascent and step size revisited

European Journal of Operational Research

Volumn 159, Issue 1, 2004, Pages 121-131

  Kleijnen, Jack P C ^a   Den Hertog, Dick ^a   Angün, Ebru ^a  

^a TILBURG UNIVERSITY   (Netherlands)

Author keywords

Heuristics; Metaheuristics; RSM; Scale dependence; Statistical analysis

Indexed keywords

HEURISTIC METHODS; MATHEMATICAL PROGRAMMING; MONTE CARLO METHODS; POLYNOMIALS; PROBLEM SOLVING; STATISTICAL METHODS;

HEURISTICS; METAHEURISTICS; RESPONSE SURFACE METHODOLOGY (RSM); SCALE-DEPENDENCE;

OPERATIONS RESEARCH;

EID: 2942653002     PISSN: 03772217     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ejor.2004.10.024     Document Type: Erratum

References (25)

1. Angün, E., den Hertog, D., Gürkan, G., Kleijnen, J.P.C., 2003. Response surface methodology with stochastic constrains for expensive simulation. Discussion Paper, Tilburg University (see http://center.kub.nl/ staff/kleijnen/).
2. Box G.E.P. Statistics as a catalyst to learning by scientific method, part II - a discussion. Journal of Quality Technology. 31(1):1999;16-29.
3. Box G.E.P., Wilson K.B. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society, Series B. 13(1):1951;1-38.
4. Conn A.R., Gould N., Toint Ph.L. Trust-region methods. 2000;SIAM, Philadelphia.
5. Donohue J.M., Houck E.C., Myers R.H. Simulation designs and correlation induction for reducing-order bias in first-order response surfaces. Operations Research. 41(5):1993;880-902.
6. Donohue J.M., Houck E.C., Myers R.H. Simulation designs for the estimation of response surface gradients in the presence of model misspecification. Management Science. 41(2):1995;244-262.
7. Driessen, L., Brekelmans, R., Hamers, H., den Hertog, D., 2001. On D-optimality based trust regions for black-box optimization problems. CentER Discussion Paper No. 2001-69, Tilburg University.
8. Fu M.C. Optimization for simulation: Theory vs. practice. INFORMS Journal on Computing. 14:2002;192-215.
9. Hood, S.J., Welch, P.D., 1993. Response surface methodology and its application in simulation. In: Evans, G.W., Mollaghasemi, M., Russell, E.C., Biles, W.E. (Eds.), Proceedings of the 1993 Winter Simulation Conference, pp. 115-122.
10. Irizarry M., Wilson J.R., Trevino J. A flexible simulation tool for manufacturing-cell design, II: Response surface analysis and case study. IIE Transactions. 33:2001;837-846.
11. Jones D.R., Schonlau M., Welch W.J. Efficient global optimization of expensive black-box functions. Journal of Global Optimization. 13:1998;455-492.
12. Khuri A.I. Multiresponse surface methodology. Ghosh S., Rao C.R. Handbook of Statistics. vol. 13:1996;Elsevier, Amsterdam.
13. Khuri A.I., Cornell J.A. Response Surfaces: Designs and Analyses. second ed. 1996;Marcel Dekker, Inc. New York.
14. Kleijnen J.P.C. Statistical Techniques in Simulation, Part II. 1975;Marcel Dekker, Inc. New York.
15. Kleijnen J.P.C. Statistical Tools for Simulation Practitioners. 1987;Marcel Dekker, Inc. New York.
16. Kleijnen J.P.C. Simulation and optimization in production planning: A case study. Decision Support Systems. 9:1993;269-280.
17. Kleijnen J.P.C. Experimental design for sensitivity analysis, optimization, and validation of simulation models. Banks J. Handbook of Simulation. 1998;173-223 Wiley, New York.
18. Law A.M., Kelton W.D. Simulation Modeling and Analysis. third ed. 2000;McGraw-Hill, Boston.
19. McDaniel W.R., Ankenman B.E. A response surface test bed. Quality and Reliability Engineering International. 16:2000;363-372.
20. Myers R.H. Response surface methodology - current status and future directions. (Including Discussion) Journal of Quality Technology. 31(1):1999;30-74.
21. Myers R.H., Montgomery D.C. Response Surface Methodology: Process and Product Optimization Using Designed Experiments. second ed. 2002;Wiley, New York.
22. Neddermeijer, H.G., van Ootmarsum, G.J., Piersma, N., Dekker, R., 2000. A framework for response surface methodology for simulation optimization models. In: Joines, J.A., Barton, R.R., Kang, K., Fishwick, P.A. (Eds.), Proceedings of the 2000 Winter Simulation Conference, pp. 129-136.
23. Sacks J., Welch W.J., Mitchell T.J., Wynn H.P. Design and analysis of computer experiments. (Includes comments and rejoinder) Statistical Science. 4(4):1989;409-435.
24. Safizadeh M.H. Minimizing the bias and variance of the gradient estimate in RSM simulation studies. European Journal of Operational Research. 136(1):2002;121-135.
25. Simpson T.W., Mauery T.M., Korte J.J., Mistree F. Kriging metamodels for global approximation in simulation-based multidisciplinary design optimization. American Institute of Aeronautics and Astronautics Journal. 39(12):2001;2233- 2241.