Budget allocation for effective data collection in predicting an accurate DEA efficiency score

Wai Peng Wong, Wikrom Jaruphongsa, Loo Hay Lee

Research output: Contribution to journalArticleResearchpeer-review

11 Citations (Scopus)

Abstract

We analyze how to allocate the budget for data collection effectively when data envelopment analysis (DEA) is used for predicting the efficiency. We formulate this problem under a Bayesian framework and propose two heuristics algorithms, i.e., a gradient-based algorithm and a hybrid GA algorithm to solve this optimization problem. Our results indicate that effective allocation of budget for data collection can greatly reduce the overall data collection effort in comparison with a uniform budget allocation.

Original languageEnglish
Pages (from-to)1235-1246
Number of pages12
JournalIEEE Transactions on Automatic Control
Volume56
Issue number6
DOIs
Publication statusPublished - Jun 2011
Externally publishedYes

Keywords

  • Budget allocation
  • genetic algorithm
  • gradient search
  • optimal computing budget allocation algorithms (OCBA)
  • stochastic data envelopment analysis (DEA)

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