Budget Allocation for Effective Data Collection in Predicting an Accurate DEA Efficiency Score

Author

Wong, Wai Peng ; Jaruphongsa, Wikrom ; Lee, Loo Hay

Author_Institution

Sch. of Manage., Univ. Sains Malaysia, Pulau, Malaysia

Volume

56

Issue

6

fYear

2011

fDate

6/1/2011 12:00:00 AM

Firstpage

1235

Lastpage

1246

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.

Keywords

data envelopment analysis; genetic algorithms; gradient methods; Bayesian framework; budget allocation; data collection; data envelopment analysis; gradient-based algorithm; heuristics algorithms; hybrid GA algorithm; optimization problem; Algorithm design and analysis; Approximation methods; Computational modeling; Data models; Monte Carlo methods; Resource management; Stochastic processes; Budget allocation; genetic algorithm; gradient search; optimal computing budget allocation algorithms (OCBA); stochastic data envelopment analysis (DEA);

fLanguage

English

Journal_Title

Automatic Control, IEEE Transactions on

Publisher

ieee

ISSN

0018-9286

Type

jour

DOI

10.1109/TAC.2010.2088870

Filename

5605659