Approximately Optimal Computing-Budget Allocation for subset ranking

Junqi Zhang, Zezhou Li, Cheng Wang, Di Zang, Mengchu Zhou

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


The best design among many can be selected through their accurate performance evaluation. When such evaluation is based on discrete event simulations, the design selection is extremely time-consuming. Ordinal optimization greatly speeds up this process. Optimal Computing-Budget Allocation (OCBA) has further accelerated it. Other kinds of OCBA have been introduced for reaching different goals, for example, to select the optimal subset of designs. However, facing the issue of subset ranking, which is a generalized form from problems selecting the best design or optimal subset, all the existing ones are insufficient. This work develops a new OCBA-based approach to address this subset ranking issue. Through mathematical deduction, its theoretical foundation is laid. Our numerical simulation results reveal that it indeed outperforms all the other existing methods in terms of probability of correct subset ranking and computational efficiency.

Original languageEnglish (US)
Article number7139736
Pages (from-to)3856-3861
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
Issue numberJune
StatePublished - Jun 29 2015
Event2015 IEEE International Conference on Robotics and Automation, ICRA 2015 - Seattle, United States
Duration: May 26 2015May 30 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


  • Discrete-event system
  • Optimal computing-budget allocation (OCBA)
  • Ranking and selection


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