 Efficiency Measurement in Data Envelopment Analysis with Fuzzy DataChiang Kao ()
Chapter 12 in Data Envelopment Analysis, 2015, pp 341-354 from Springer Abstract: Abstract Conventional data envelopment analysis (DEA) requires the data to have crisp values, which can be measured precisely. However, there are cases where data is missing and has to be estimated, or the situation has not occurred yet and the data has to be predicted. There are also cases where the factors are qualitative, and thus the data cannot be measured precisely. In these cases, fuzzy numbers can be used to represent the imprecise values, and this paper discusses the corresponding measurement of efficiency. Based on the extension principle, two approaches are proposed; one views the membership function of the fuzzy data vertically, and the results are represented by membership grades. The other views it horizontally, and the results are represented by α-cuts. The former approach is easier to understand, yet is applicable only to very simple problems. The latter, in contrast, can be applied to all problems, and is easier to implement. An example explains the development and implementation of these two approaches. Keywords: Data envelopment analysis; Fuzzy data; Two-level programming; Extension principle (search for similar items in EconPapers) There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-1-4899-7553-9_12 Ordering information: This item can be ordered from DOI: 10.1007/978-1-4899-7553-9_12 Access Statistics for this chapter More chapters in International Series in Operations Research & Management Science from Springer |
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