Data envelopment analysis with imprecise data

2002 ◽  
Vol 140 (1) ◽  
pp. 24-36 ◽  
Author(s):  
Dimitris K. Despotis ◽  
Yiannis G. Smirlis
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Imprecise Data

Imprecise data envelopment analysis model with bifuzzy variables

2014 ◽  
Vol 27 (1) ◽  
pp. 37-48 ◽  
Author(s):  
Khalil Paryab ◽  
Rashed Khanjani Shiraz ◽  
Leila Jalalzadeh ◽  
Hirofumi Fukuyama
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Analysis Model ◽  
Imprecise Data Envelopment Analysis

DATA ENVELOPMENT ANALYSIS WITH IMPRECISE DATA: AN APPLICATION OF TAIWAN MACHINERY FIRMS

2005 ◽  
Vol 13 (02) ◽  
pp. 225-240 ◽  
Author(s):  
CHIANG KAO ◽  
SHIANG-TAI LIU
Keyword(s):  
Data Envelopment Analysis ◽  
Production Management ◽  
Fuzzy Numbers ◽  
Major Influence ◽  
Data Envelopment ◽  
Fuzzy Data ◽  
Imprecise Data ◽  
Fuzzy Data Envelopment Analysis ◽  
Automation Technology ◽  
Technology Level

Technology and management are two broad categories of factors that have major influence on the productivity of manufacturing firms. The ratio of the actual productivity of a firm to its maximal productivity is a measure of efficiency. Since the automation technology level and production management achievement of a firm cannot be measured precisely, a fuzzy data envelopment analysis (FDEA) model is applied to calculate the efficiency scores of a sample of machinery firms in Taiwan. Different from the conventional crisp DEA, the efficiency scores calculated from the FDEA model are fuzzy numbers, in that ranges of values at different possibility levels rather than a single crisp value are provided. When the observations are fuzzy in nature, the FDEA methodology avoids the top management from being over-confident with the results as opposed to that of using the conventional DEA methodology by simplifying the fuzzy observations to crisp values.

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