Approximately Optimal Computing Budget Allocation for Selection of the Best and Worst Designs

Junqi Zhang, Liang Zhang, Cheng Wang, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Ordinal optimization is an efficient technique to choose and rank various engineering designs that require time-consuming discrete-event simulations. Optimal computing budget allocation (OCBA) has been an important tool to enhance its efficiency such that the best design is selected in a timely fashion. It, however, fails to address the issue of selecting the best and worst designs efficiently. The need to select both rapidly given a fixed computing budget has arisen from many applications. This work develops a new OCBA-based approach for selecting both best and worst designs at the same time. Its theoretical foundation is laid. Our numerical results show that it can well outperform all the existing methods in terms of probability of correct selection and computational efficiency.

Original languageEnglish (US)
Article number7742364
Pages (from-to)3249-3261
Number of pages13
JournalIEEE Transactions on Automatic Control
Volume62
Issue number7
DOIs
StatePublished - Jul 2017

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Design selection
  • discrete-event simulation and optimization
  • discrete-event systems
  • optimal computing budget allocation

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