Data Envelopment Analysis and Big Data: Revisit with a Faster Method

Dariush Khezrimotlagh () and Joe Zhu ()
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Dariush Khezrimotlagh: Pennsylvania State University - Harrisburg
Joe Zhu: Worcester Polytechnic Institute

Chapter Chapter 1 in Data Science and Productivity Analytics, 2020, pp 1-34 from Springer

Abstract: Abstract Khezrimotlagh et al. (Eur J Oper Res 274(3):1047–1054, 2019) propose a new framework to deal with large-scale data envelopment analysis (DEA). The framework provides the fastest available technique in the DEA literature to deal with big data. It is well known that as the number of decision-making units (DMUs) or the number of inputs–outputs increases, the size of DEA linear programming problems increases; and thus, the elapsed time to evaluate the performance of DMUs sharply increases. The framework selects a subsample of DMUs and identifies the set of all efficient DMUs. After that, users can apply DEA models with known efficient DMUs to evaluate the performance of inefficient DMUs or benchmark them. In this study, we elucidate their proposed method with transparent examples and illustrate how the framework is applied. Additional simulation exercises are designed to evaluate the performance of the framework in comparison with the performance of the two former methods: build hull (BH) and hierarchical decomposition (DH). The disadvantages of BH and HD are transparently demonstrated. A single computer with two different CPUs is used to run the methods. For the first time in the literature, we consider the cardinalities, 200,000, 500,000 and 1,000,000 DMUs.

Keywords: Performance evaluation; Big data; Data envelopment analysis (DEA); Simulation (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/978-3-030-43384-0_1

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