ISSN:
1573-7594
Keywords:
discrete event dynamic systems
;
stochastic optimization
;
response surface
;
gradient surface
;
perturbation analysis
;
likelihood ratios
;
single-run optimization
Source:
Springer Online Journal Archives 1860-2000
Topics:
Mathematics
Notes:
Abstract In this paper we propose a gradient surface method (GSM) for the optimization of discrete event dynamic systems. GSM combines the advantages of response surface methodology (RSM) and efficient derivative estimation techniques like perturbation analysis (PA) or likelihood ratio method (LR). In GSM, the gradient estimation is obtained by PA (or LR), and the performance gradient surface is obtained from observations at various points in a fashion similar to the RSM. Zero points of the successive approximating gradient surface are then taken as the estimates of the optimal solution. GSM is characterized by several attractive features: it is a single-run method and more efficient than RSM; it uses at each iteration step the information from all data points rather than just the local gradient; it tries to capture the global features of the gradient surface and thereby quickly arrives at the vicinity of the optimal solution. A number of examples are exhibited to illustrate this method.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF01797723
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