Ranking DMUs by ideal points with interval data in DEA

Jahanshahloo, G. R.; Lotfı, Farhad Hosseinzadeh; Rezaie, Vahideh; Khanmohammadi, M.

doi:10.1016/j.apm.2010.05.019

Cited by 41 publications

(17 citation statements)

References 20 publications

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“…Several approaches have been introduced to deal with imprecise, uncertain, incomplete, and/or unavailable data in DEA. Among them, stochastic methods (Cooper, Deng, Hang, & Li, ; Khodabakhshi, Asgharian, & Gregoria, ) and interval DEA models are commonly used to identify imprecise input and output data (Entani & Tanaka, ; Jahanshahloo, Hosseinzadeh, Rezaie, & Khanmohammadi, ; Smirlis, Maragos, & Despotis, ). However, the fuzzy theory introduced by Zadeh () has been predominantly applied for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Data envelopment analysis with neutrosophic inputs and outputs

Abdelfattah

2019

Expert Systems

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Since the recent appearance of neutrosophic theory as a generalization of fuzzy and intuitionistic fuzzy theories, many multicriteria decision methods have adopted this theory to deal with incomplete and indeterminate data. However, it has not yet been applied to the data envelopment analysis (DEA) methodology. Therefore, this study presents a DEA model with triangular neutrosophic inputs and outputs that considers the truth, indeterminacy, and falsity degrees of each data value. As an alternative, a parametric approach based on what we term the variation degree of a triangular neutrosophic number is developed. This approach transforms a neutrosophic DEA model into an interval DEA model that can be solved using one of many existing techniques. Interval efficiency scores obtained from our numerical example show the flexibility and authenticity of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

Data envelopment analysis with neutrosophic inputs and outputs

Abdelfattah

2019

Expert Systems

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“…They made two hypothetical DMUs namely the ideal and nadir DMUs as reference points to rank the DMUs. Jahanshahloo et al [40] proposed an interval DEA model to attain an efficiency interval, including evaluations from both the optimistic and the pessimistic perspectives. In their method, the lower bounds of the DMUs are increased to obtain the maximum value.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation

Nikfarjam

Rostamy-Malkhalifeh

Noura

2018

Advances in Operations Research

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Supplier selection is one of the intricate decisions of managers in modern business era. There are different methods and techniques for supplier selection. Data envelopment analysis (DEA) is a popular decision-making method that can be used for this purpose. In this paper, a new dynamic DEA approach is proposed which is capable of evaluating the suppliers in consecutive periods based on their inputs, outputs, and the relationships between the periods classified as desirable relationships, undesirable relationships, and free relationships with positive and negative natures. To this aim various social, economic, and environmental criteria are taken into account. A new method for constructing an ideal decision-making unit (DMU) is proposed in this paper which differs from the existing ones in the literature according to its capability of considering periods with unit efficiencies which do not necessarily belong to a unique DMU. Furthermore, the new ideal DMU has the required ability to rank the suppliers with the same efficiency ratio. In the concerned problem, the supplier that has unit efficiency in each period is selected to construct an ideal supplier. Since it is possible to have more than one supplier with unit efficiency in each period, the ideal supplier can be made with different scenarios with a given probability. To deal with such uncertain condition, a new robust dynamic DEA model is elaborated based on a scenario-based robust optimization approach. Computational results indicate that the proposed robust optimization approach can evaluate and rank the suppliers with unit efficiencies which could not be ranked previously. Furthermore, the proposed ideal DMU can be appropriately used as a benchmark for other DMUs to adjust the probable improvement plans.

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“…Interval data are also commonly used to identify imprecise input and output data (Entani et al, 2002;Jahanshahloo et al, , 2011Jahanshahloo and Khodabakhshi, 2004;Smirlis et al, 2006;Entani and Tanaka, 2006). Entani et al (2002) formulated a DEA model with interval efficiency which consisted of efficiencies obtained from the optimistic and pessimistic viewpoints.…”

Section: Introductionmentioning

confidence: 99%

“…They formulated an interval DEA model and obtained an efficiency interval consisting of evaluations from both the optimistic and pessimistic viewpoints. Jahanshahloo et al (2011) formulated an interval DEA model to obtain an efficiency interval consisting of evaluations from both the optimistic and the pessimistic viewpoints. The points obtained by this method are called ideal points which are used to rank the DMUs.…”

Section: Introductionmentioning

confidence: 99%

Optimistic and pessimistic performance and congestion analysis in fuzzy data envelopment analysis

Khodabakhshi

Tavana

Abootaleb

2016

IJLSM

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The notion of input congestion in data envelopment analysis (DEA) is analogous to the 'law of diminishing returns' in the classical economic theory of production which states that if a single input is increased while other inputs are held constant, the marginal product of the variable input diminishes. Congestion has been an under-researched topic in economic theory especially when there is a need for augmenting inputs to serve important objectives besides output maximisation. We propose a fuzzy DEA model and represent the imprecise and ambiguous input and output data with fuzzy numbers. We solve the model with an α-cut approach and obtain the value of input congestion for the optimistic and pessimistic cases. The fundamental idea in this paper is to transform the fuzzy DEA model into a crisp linear programming 2 M Khodabakhshi et al. model using the α-cut approach. Two auxiliary crisp models are solved to obtain optimistic and pessimistic values of congestion for evaluating the decision-making units (DMUs). We use a numerical example from the literature to demonstrate the applicability of the proposed method and exhibit the efficacy of the procedures and algorithms.

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Ranking DMUs by ideal points with interval data in DEA

Cited by 41 publications

References 20 publications

Data envelopment analysis with neutrosophic inputs and outputs

Data envelopment analysis with neutrosophic inputs and outputs

A New Robust Dynamic Data Envelopment Analysis Approach for Sustainable Supplier Evaluation

Optimistic and pessimistic performance and congestion analysis in fuzzy data envelopment analysis

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