Fuzzy stochastic data envelopment analysis with application to base realignment and closure (BRAC)

Tavana, Madjid; Shiraz, Rashed Khanjani; Hatami-Marbini, Adel; Agrell, Per Joakim; Paryab, Khalil

doi:10.1016/j.eswa.2012.04.049

Cited by 46 publications

(19 citation statements)

References 34 publications

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“…This paper utilizes the concept of chance-constrained programming, which was introduced by Charnes and Cooper [3], as a way to solve a fuzzy DEA model. Similar to Tavana et al [21], the current study considers the CCR model with fuzzy possibility-possibility (Pos-Pos) constraints constructed as follows:…”

Section: Bifuzzy Chance Constrained Ccr Modelmentioning

confidence: 99%

“…Wu et al [25] applied a possibility DEA model for efficiency analysis of cross-region bank branches in Canada. Tavana et al [21] proposed both stochastic and fuzzy DEA models, and obtained their crisp equivalent models. Wang and Chin [24] presented a fuzzy expected value approach for fuzzy DEA frontiers.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, output 2 is a bifuzzy variableỹ 21 = (ȳ 21 , 45) withȳ 21 = (2456, 135) whereȳ 21 is a central value and 45 is a spread value forỹ21 .To further explain the decision making in Example 2, consider that the EPG company's research division forecasts that sales income from the commercial customers for DMU 1 is "between 2411 and 2501 dollars" and the most likely value is 2456. The statement "between 2411 and 2501 dollars" is an example of imprecise information in the decision-making process, which can be translated into an interval [2411 2501].…”

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confidence: 99%

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Imprecise data envelopment analysis model with bifuzzy variables

Paryab

Shiraz

Jalalzadeh

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

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Data envelopment analysis (DEA) is a non-parametric approach for measuring and evaluating the relative efficiencies of a set of entities with common crisp inputs and outputs. Whereas crisp input-output data are required in the traditional DEA evaluation process, the observed input-output data in real-world performance evaluation problems are quite often imprecise or vague. The impreciseness and vagueness related to the input-output data in DEA can be represented by fuzzy variables. The purpose of this paper is three-fold. First, the current study introduces a non-deterministic chance constrained DEA model, which solves the Charnes, Cooper and Rhodes (CCR) model by treating the input-output data as bifuzzy variables which are fuzzy variables with fuzzy parameters. Second, by assuming that decision making units operate in a bifuzzy environment, the study derives a deterministic model equivalent to the chance constrained model. Finally, two numerical examples are presented to demonstrate the applicability of the proposed framework.

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Section: Bifuzzy Chance Constrained Ccr Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Imprecise data envelopment analysis model with bifuzzy variables

Paryab

Shiraz

Jalalzadeh

et al. 2014

Journal of Intelligent &Amp; Fuzzy Systems

Self Cite

View full text Add to dashboard Cite

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“…Tavana et al [33] developed three imprecise DEA models in the presence of probability-possibility, probabilitynecessity and probability-credibility constraints where fuzziness and randomness simultaneously exist in an evaluation problem. Tavana et al [34] introduced random fuzzy variables in DEA when randomness and vagueness coexist in the same problem.…”

Section: Introductionmentioning

confidence: 99%

“…In such cases, the concept of random-rough variable turns out to be a useful tool in dealing with these two types of uncertainty simultaneously. Recently Khanjani et al [12], Tavana et al ( [33,34], 2014) and Paryab et al [23] presented DEA models with two-fold uncertain data. Khanjani et al [12] proposed fuzzy rough DEA models based on the expected value and possibility approaches.…”

Section: Introductionmentioning

confidence: 99%

Chance-constrained data envelopment analysis modeling with random-rough data

2018

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Data envelopment analysis (DEA) is a useful management tool for measuring the relative efficiency of decision making units (DMUs) which consumes multiple inputs to produce multiple outputs. Although precise input and output data are fundamentally indispensable in classical DEA models, real-world problems often involve random and/or rough input and output data. We present a chance-constrained DEA model with random and rough (random-rough) input and output data and propose a deterministic equivalent model with quadratic constraints to solve the model. The main contributions of this paper are fourfold: (3.1) we propose a DEA model for problems characterized by random-rough variables; (3.2) we transform the proposed chance-constrained model with random-rough variables into a deterministic equivalent non-linear form that could be simplified as a deterministic model with quadratic constraints; (3.3) we perform sensitivity analysis to investigate the stability and robustness of the proposed model; and (3.4) we use a numerical example to demonstrate the feasibility and richness of the obtained solutions.

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