Optimal Computing Cost Budget Allocation For Solving Buffer Allocation Problem

In this paper, we study the problem of selecting a system that has the best performance or selecting one of the best systems when the objective function is the expected performance of a complex stochastic system. The focus here is on a huge size but finite feasible solution set, that is not necessarily well structured. We consider the use of the ordinal optimization technique that concerns with the order of the designs in the state space rather than estimating each design alternative accurately. We also assume that the computing cost is limited. We discuss how to allocate the available computational resources on the alternative designs to maximize the probability of making a correct selection (selecting the best or one of the best systems).