Combined variance reduction techniques in fully sequential selection procedures

Shing Chih Tsai, Jun Luo, Chi Ching Sung

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In the past several decades, many ranking-and-selection (R&S) procedures have been developed to select the best simulated system with the largest (or smallest) mean performance measure from a finite number of alternatives. A major issue to address in these R&S problems is to balance the trade-off between the effectiveness (ie, making a correct selection with a high probability) and the efficiency (ie, using a small total number of observations). In this paper, we take a frequentist's point of view by setting a predetermined probability of correct selection while trying to reduce the total sample size, that is, to improve the efficiency but also maintain the effectiveness. In particular, in order to achieve this goal, we investigate combining various variance reduction techniques into the fully sequential framework, resulting in different R&S procedures with either finite-time or asymptotic statistical validity. Extensive numerical experiments show great improvement in the efficiency of our proposed procedures as compared with several existing procedures.

Original languageEnglish
Pages (from-to)502-527
Number of pages26
JournalNaval Research Logistics
Volume64
Issue number6
DOIs
Publication statusPublished - 2017 Sep

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Ocean Engineering
  • Management Science and Operations Research

Fingerprint Dive into the research topics of 'Combined variance reduction techniques in fully sequential selection procedures'. Together they form a unique fingerprint.

Cite this