Determining a common set of weights in a DEA problem using a separation vector
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Publication:409931
DOI10.1016/j.mcm.2011.06.002zbMath1235.90079OpenAlexW1991699093MaRDI QIDQ409931
Publication date: 15 April 2012
Published in: Mathematical and Computer Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.mcm.2011.06.002
Multi-objective and goal programming (90C29) Fractional programming (90C32) Linear programming (90C05)
Related Items (11)
A compromise programming approach for target setting in DEA ⋮ Common set of weights and efficiency improvement on the basis of separation vector in two-stage network data envelopment analysis ⋮ Multiobjective centralized DEA approach to Tokyo 2020 Olympic Games ⋮ A novel ranking approach with common weights: An implementation in the presence of interval data and flexible measures ⋮ A new fuzzy additive model for determining the common set of weights in Data Envelopment Analysis ⋮ A novel approach for discriminating efficient candidates by classifying voters in the preferential voting framework ⋮ A method for evaluating the performance of decision making units with imprecise data using common set of weights ⋮ Resource allocation: a common set of weights model ⋮ New common set of weights method in black-box and two-stage data envelopment analysis ⋮ A new method for ranking decision making units using common set of weights: a developed criterion ⋮ Measuring Olympics achievements based on a parallel DEA approach
Cites Work
- Within-group common weights in DEA: an analysis of power plant efficiency
- Olympic ranking based on a zero sum gains DEA model.
- Ranking of units on the DEA frontier with common weights
- A note on some of DEA models and finding efficiency and complete ranking using common set of weights
- Towards fair ranking of olympics achievements: the case of Sydney 2000
- Data envelopment analysis with common weights: the compromise solution approach
- Measuring the performance of nations at the Summer Olympics using data envelopment analysis
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