Please use this identifier to cite or link to this item: https://doi.org/10.1109/TAC.2010.2088870
Title: Budget allocation for effective data collection in predicting an accurate DEA efficiency score
Authors: Wong, W.P.
Jaruphongsa, W.
Lee, L.H. 
Keywords: Budget allocation
genetic algorithm
gradient search
optimal computing budget allocation algorithms (OCBA)
stochastic data envelopment analysis (DEA)
Issue Date: Jun-2011
Citation: Wong, W.P., Jaruphongsa, W., Lee, L.H. (2011-06). Budget allocation for effective data collection in predicting an accurate DEA efficiency score. IEEE Transactions on Automatic Control 56 (6) : 1235-1246. ScholarBank@NUS Repository. https://doi.org/10.1109/TAC.2010.2088870
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. © 2006 IEEE.
Source Title: IEEE Transactions on Automatic Control
URI: http://scholarbank.nus.edu.sg/handle/10635/63047
ISSN: 00189286
DOI: 10.1109/TAC.2010.2088870
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