Article ID: | iaor1998385 |
Country: | Netherlands |
Volume: | 75 |
Issue: | 2 |
Start Page Number: | 137 |
End Page Number: | 176 |
Publication Date: | Nov 1996 |
Journal: | Mathematical Programming |
Authors: | Suri Rajan, Robinson Stephen M., Plambeck Erica L., Fu Bor-Ruey |
Keywords: | programming: convex |
In this paper we propose a method for optimizing convex performance functions in stochastic systems. These functions can include expected performance in static systems and steady-state performance in discrete-event dynamic systems; they may be nonsmooth. The method is closely related to retrospective simulation optimization; it appears to overcome some limitations of stochastic approximation, which is often applied to such problems. We explain the method and give computational results for two classes of problems: tandem production lines with up to 50 machines, and stochastic PERT (Program Evaluation and Review Technique) problems with up to 70 nodes and 110 arcs.