  Simulation-based ranking and selection (R&S) is a popular technique for
optimizing discrete-event systems (DESs). It evaluates the mean performance of
system designs by simulation outputs and aims to identify the best system
design from a finite set of alternatives by intelligently allocating a limited
simulation budget. In R&S, the optimal computing budget allocation (OCBA) is an
efficient budget allocation rule that asymptotically maximizes the probability
of correct selection (PCS). However, the OCBA allocation rule ignores the
impact of budget size, which plays an important role in finite budget
allocation. To address this, we develop a budget allocation rule that is
adaptive to the simulation budget. Theoretical results show that the proposed
allocation rule can dynamically determine the ratio of budget allocated to
designs based on different simulation budgets and achieve asymptotic
optimality. Furthermore, the finite-budget properties possessed by our proposed
allocation rule highlight the significant differences between finite budget and
sufficiently large budget allocation strategies. Based on the proposed
budget-adaptive allocation rule, two heuristic algorithms are developed. In the
numerical experiments, we use both synthetic examples and a case study to show
the superior efficiency of our proposed allocation rule.
