An asymptotic test of optimality conditions in multiresponse simulation optimization
This paper derives a novel, asymptotic statistical test of the Karush--Kuhn--Tucker first-order necessary optimality conditions in random simulation models with multiple responses. This test combines a simple form of the delta method and a generalized version of Wald's statistic. The test is applied to both a toy problem and an ( s , S ) inventory-optimization problem with a service-level constraint; its numerical results are encouraging.
(This abstract was borrowed from another version of this item.)
|Date of creation:||2012|
|Publication status:||Published in INFORMS Journal on Computing (2012), v.24, nr.1, p.53-65|
|Contact details of provider:|| Web page: https://www.tilburguniversity.edu/about/schools/economics-and-management/|
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- Bettonvil, Bert & del Castillo, Enrique & Kleijnen, Jack P.C., 2009. "Statistical testing of optimality conditions in multiresponse simulation-based optimization," European Journal of Operational Research, Elsevier, vol. 199(2), pages 448-458, December.
- Sridhar Bashyam & Michael C. Fu, 1998. "Optimization of (s, S) Inventory Systems with Random Lead Times and a Service Level Constraint," Management Science, INFORMS, vol. 44(12-Part-2), pages 243-256, December.
- Safizadeh, M. Hossein, 2002. "Minimizing the bias and variance of the gradient estimate in RSM simulation studies," European Journal of Operational Research, Elsevier, vol. 136(1), pages 121-135, January.
- Kodde, David A & Palm, Franz C, 1986. "Wald Criteria for Jointly Testing Equality and Inequality Restriction s," Econometrica, Econometric Society, vol. 54(5), pages 1243-1248, September.
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