Sample-path optimization of convex stochastic performance functions

Sample-path optimization of convex stochastic performance functions

By
Erica Plambeck, Bor-Ruey Fu, Stephen M. Robinson, Rajan Suri
Mathematical Programming. November
30, 1996, Vol. 75, Issue 2, Pages 137-179

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.