A new ranking method to fuzzy data envelopment analysis

Wen, Meilin; You, Cuilian; Kang, Rui

doi:10.1016/j.camwa.2010.02.034

Cited by 45 publications

(15 citation statements)

References 22 publications

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“…This section aims to make the model easy to compute when the membership function of the fuzzy inputs and outputs have some particular characters. The model can be solved by applying the theorems given by Wen et al (2010). According to theorem, suppose that ξ 1 , ξ 2 are fuzzy variables defined on credibility space (Θ, P(Θ), Cr).…”

Section: A Special Casementioning

confidence: 99%

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Scale efficiency with fuzzy data

Agarwal

2017

IJBSR

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This paper measures the scale efficiency of decision-making units (DMUs) with fuzzy input and output data using fuzzy data envelopment analysis (FDEA) model. FDEA is used to measure the relative efficiencies of a set of DMUs under fuzzy environment in which the input and output data can be represented as linguistic variable characterised by fuzzy variables. The FDEA is solved using the concept of chance-constrained programming and credibility approach. In a special case, when fuzzy inputs and fuzzy outputs are independent trapezoidal or triangular fuzzy variables, the model can be transformed into crisp linear programming. Finally, numerical illustration is presented to illustrate the FDEA model to measure the scale efficiency of DMUs with fuzzy data as well as the effectiveness of the presented method.

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Section: A Special Casementioning

confidence: 99%

“…When fuzzy inputs and fuzzy outputs are trapezoidal fuzzy numbers, then the credibility fuzzy BCC DEA model (5) become the following linear programming model by using the concept given by Liu (2006) and the above theorem given by Wen et al (2010) for any given 0.5 ≤ α ≤ 1. (1 ) 1 2(1 ) 1 0 2(1 ) 2 (1 ) …”

Section: A Special Casementioning

confidence: 99%

Scale efficiency with fuzzy data

Agarwal

2017

IJBSR

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“…Zerafat Angiz et al [37] proposed a six-stage algorithm to rank the efficient DMUs using fuzzy concept. To see the other FDEA models, the readers can see [13,34,35,39]. This paper is organized in four additional sections.…”

Section: Introductionmentioning

confidence: 99%

Ranking units with fuzzy data in DEA

Khodabakhshi¹,

Aryavash²

2014

Data Envelopment Analysis and Decision Science

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In this study, both optimistic and pessimistic approaches of data envelopment analysis are applied to propose an equitable ranking method in fuzzy environments. To this end, we suppose that the sum of efficiency scores of all decision making units (DMUs) equals to unity. Using the worst-best and best-worst approaches, the minimum and maximum possible efficiency scores of each DMU are estimated at some α-levels. Then, a number of such scores are used to construct the corresponding fuzzy score. Finally, using a defuzzification method the obtained fuzzy score is transformed into crisp score. DMUs are ranked according to their crisp scores.

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“…The optimal value of z hj is achieved by substituting this parameter into Models (33) and (34). After solving Model (35), the optimal values of reduced costs of s x i , s z h , and s y are the values of multipliers v i , w h , and u , respectively.…”

Section: Lower Boundmentioning

confidence: 99%

“…Fuzzy sets, which were introduced by Zadeh [25], are one of the common methods for solving DEA with imprecise data. The applications of fuzzy set theory in DEA are usually divided into four categories [24,26]: tolerance approach [27,28], -level based approach [29,30], fuzzy ranking approach [31,32], and possibility approach [33,34].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Supply Chain of a Shipping Company in Iran by a Fuzzy Relational Network Data Envelopment Analysis Model

2017

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Abstract. The existing relational network Data Envelopment Analysis (DEA) models evaluate the performance of Decision Making Units (DMUs) with precise data. Whereas, in the real-world applications, there are many Supply Chain (SC) networks with imprecise and vague gures. This paper develops a relational network DEA model for evaluating the performance of supply chains with fuzzy numbers. The proposed fuzzy model is capable of evaluating the performance of all kinds of network structures. A pair of twolevel mathematical programs is utilized to convert the fuzzy relational network DEA to a conventional crisp one. For this purpose, the upper and lower bounds of the e ciencies are calculated by -cut concept. The proposed model is implemented using actual data from the supply chain of an international shipping company in Iran.

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A new ranking method to fuzzy data envelopment analysis

Cited by 45 publications

References 22 publications

Scale efficiency with fuzzy data

Scale efficiency with fuzzy data

Ranking units with fuzzy data in DEA

Evaluation of Supply Chain of a Shipping Company in Iran by a Fuzzy Relational Network Data Envelopment Analysis Model

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