Optimal Computing Budget Allocation Framework

TL;DR: This paper proposes an unbiased direct approximation for optimal computing budget allocation in Ranking and Selection problems, leveraging parallel hardware to improve finite performance, specifically for the Probability of Correct Selection (PCS) objective.

TL;DR: This work characterize fractional allocations that are asymptotically optimal and provides an easily implementable algorithm that results in sampling allocations that provably achieve in the limit the same performance as the optimal allocations.

TL;DR: A two-stage particle swarm optimization (PSO) algorithm for SJSSP with the objective of minimizing the expected total weighted tardiness and the optimal computing budget allocation (OCBA) method is used.

TL;DR: This paper introduces two algorithms OCBA-mSG andOCBA-bSG to identify a subset of m simplest and good enough designs among a total of K (K >; m) designs and controls the simulation allocation intelligently to find those simplest goodenough designs using a minimum simulation time.

TL;DR: This paper presents a method to minimize simulation time, yet to achieve a desirable confidence of the obtained results by applying ordinal optimization and computing budget allocation ideas and techniques, while taking into account statistical properties of the variance.