Data Envelopment Analysis for Decision Making
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".
Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2571
Special Issue Editors
Interests: economics; operational research; transportation; logistics performance evaluation; data envelopment analysis
Interests: performance evaluation; data analysis; operational research; machine learning
Special Issue Information
Dear colleagues,
Data Envelopment Analysis (DEA), initially developed by Charnes et al. (1978), is an optimization method of mathematical programming to generalize the Farrell (1957) single-input/single-output technical efficiency measure to the multiple-input/ multiple-output case by constructing a relative efficiency score as the ratio of a single virtual output to a single virtual input. Thus, DEA has become a new tool in operational research for measuring the technical efficiency of decision-making units.
Recently, DEA has been extended in both theoretical development and applications. We look forward to original analytical and empirical research using the DEA approaches with practical applications in various areas. Applications (including but not limited to) in finance, banking, healthcare, transportation, education, energy, environment, and more will be considered. New and improved models of DEA are encouraged. A Special Issue that compiles current research on the state of the art will benefit the DEA community.
Prof. Dr. Ming-Min Yu
Prof. Dr. Bo Hsiao
Dr. Li-Hsueh Chen
Guest Editors
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Keywords
- performance evaluation
- technology gaps
- benchmarking
- allocation
- reallocation
- mobility
- efficiency
- decision making
- productivity
- data reduction
- productivity
- optimization
- efficiency measurement
- data analysis
- bootstrapping
- regression analysis
- competition
- multiple activities
- network
- dynamics
- malmquist index
- returns to scale
- problem solving
- data envelopment
- mathematical programming
- numerical model
- sensitivity analysis
- total factor productivity
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