623. Fuzzy measures for fuzzy hierarchical data envelopment analysis
Invited abstract in session TB-60: DEA under uncertainty, stream Data Envelopment Analysis and its applications.
Tuesday, 10:30-12:00Room: Western LT
Authors (first author is the speaker)
| 1. | Shiang-Tai Liu
|
| Graduate School of Business & Management, Vanung University |
Abstract
Data Envelopment Analysis (DEA) is an effective method for evaluating the performance of decision-making units (DMUs). Many enterprises have units under their jurisdiction that may include further sub-units, using inputs allocated by their parent units to produce various outputs. The sum of all sub-unit outputs constitutes the parent unit's output, indicating a hierarchical relationship between units. Such systems can be referred to as hierarchy systems. In a DEA model, estimating data in practical applications is sometimes necessary due to difficulties in precise measurement. In such cases, fuzzy values can replace the input and output items. Therefore, how to calculate the efficiency scores of fuzzy hierarchical DEA under fuzzy conditions is a worthy topic for research. When both input and output values are fuzzy, the developed fuzzy hierarchical DEA efficiency scores will vary with changes in the efficiency frontier. In calculating the fuzzy hierarchical efficiency scores, it is essential to consider the relationship between the simultaneous changes in input and output values. This paper aims to derive minimum and maximum efficiency scores under varying observational values, allowing the computation of fuzzy hierarchical efficiency scores' membership functions at multiple levels. The fuzzy hierarchical DEA efficiency scores obtained in this study will also be fuzzy numbers, which align more intuitively with problem-solving and provide more information for decision-making.
Keywords
- Data Envelopment Analysis
- Efficiency Analysis
- Fuzzy Sets and Systems
Status: accepted
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