Practical Simulation Budget Allocation for Ranked Subset Partitioning

Stochastic simulation is a powerful tool for analyzing complex discrete-event dynamic systems; however, it does not exhibit sufficient efficiency because of the requirement numerous replicated simulations obtaining accurate analysis results. Ranking and selection (R&S;) efficiently allocates budget using ordinal optimization to correctly select alternatives interest. Existing R&S; methods focus on selecting an optimal alternative or subset alternatives. Based generalization this methodology, we propose method partitioning k into xmlns:xlink="http://www.w3.org/1999/xlink">n (2 ≤ ) exclusive ranked subsets, which effective job distribution web search applications. The proposed evaluates if observed results each have precision distinguish between subsets. It sequentially small portion based evaluation results, gradually improving maximize efficiency. superior compared with that existing demonstrated various numerical experiments. Furthermore, practical problem involves relocation-zone in bicycle-sharing systems demonstrates can be effectively applied situations requiring high efficiencies, such as digital twins systems.