Interval efficiency assessment using data envelopment analysis

By Yingming Wang, Richard Greatbanks, Jianbo Yang. Fuzzy Sets Syst., 2005

377 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/3ff248296738afc39608d5c497a0f6c09aff0207

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Yingming Wang
2. Kwai-Sang Chin
3. Ali Emrouznejad
4. Adel Hatami-Marbini
5. Per J. Agrell
6. F. Lotfi
7. Gholam Reza Jahanshahloo
8. M. Velasquez

In Management Science and Operations Research

1. Kwai-Sang Chin
2. Jianbo Yang
3. Yingming Wang
4. Gary Ka Kwai Poon
5. Adel Hatami-Marbini
6. Ali Emrouznejad
7. Per J. Agrell
8. F. Lotfi

Citations in the corpus

• Data envelopment analysis 1978-2010: A citation-based literature survey (2013, background): “[70] propose a general model to deal with interval, ordinal, and fuzzy data.”
• Failure mode and effects analysis using a group-based evidential reasoning approach (2009, methodology): “[41] is a method for comparing and ranking interval numbers and is briefly summarized below for the sake of application.”
• An Analysis of Multi-Criteria Decision Making Methods (2013, background): “In real world situations, however, this assumption may not always be true " (Wang, Greatbanks, and Yang, 2005, p. 348).”
• Interval data without sign restrictions in DEA (2014, methodology): “[29] constructed a pair of interval DEAmodels without the need of extra variable alternations and used a fixed and unified production frontier to measure the efficiencies of DMUs with interval input and output data.”
• A taxonomy and review of the fuzzy data envelopment analysis literature: Two decades in the making (2011, background): “Wang et al. (2005) proposed a pair of interval DEA models for dealing with imprecise data such as interval data, ordinal preference information, fuzzy data and their mixture.”
• Ranking DMUs by ideal points with interval data in DEA (2011, background): “[16] have developed a new pair of interval DEAmodels with imprecise data in a simple, rational and effective way.”
• A new benchmarking approach in Cold Chain (2012, background): “[52], developed a new pair of interval DEA models for handling the imprecise data.”