Ordinal optimization concentrates on isolating a subset of good designs with high probability and reduces the required simulation time dramatically for discrete event simulation. To obtain the same probability level,w...Ordinal optimization concentrates on isolating a subset of good designs with high probability and reduces the required simulation time dramatically for discrete event simulation. To obtain the same probability level,we may optimally allocate our computing budget among different designs,instead of equally simulating all different designs. In this paper we present an effective approach to optimally allocate computing budget for discrete-event system simulation. While ordinal optimization can dramatically reduce the computation cost, our approach can further reduce the already-low cost.展开更多
文摘Ordinal optimization concentrates on isolating a subset of good designs with high probability and reduces the required simulation time dramatically for discrete event simulation. To obtain the same probability level,we may optimally allocate our computing budget among different designs,instead of equally simulating all different designs. In this paper we present an effective approach to optimally allocate computing budget for discrete-event system simulation. While ordinal optimization can dramatically reduce the computation cost, our approach can further reduce the already-low cost.
