Fuzzy rough DEA model: A possibility and expected value approaches

Shiraz, Rashed Khanjani; Charles, Vincent; Jalalzadeh, Leila

doi:10.1016/j.eswa.2013.07.069

Cited by 44 publications

(11 citation statements)

References 33 publications

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“…Guo and Tanaka (2001), León et al (2003) and Lertworasirikul et al (2003) presented FDEA models with uncertainties in fuzzy objectives and fuzzy constraints. The literature on FDEA has been previously reported by Kao and Liu (2000), Saati et al (2002), Wang et al (2009), Hatami-Marbini et al (2011a, Wang and Chin (2011), Shen et al (2011), Angiz et al (2012, Puri and Yadav (2013;, Shiraz et al (2014) and Emrouznejad et al (2014). Several approaches have been developed to deal with fuzzy parameters, i.e., fuzzy input and output data in FDEA.…”

Section: Introductionmentioning

confidence: 87%

A fully fuzzy approach to DEA and multi-component DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs

Puri

Yadav

2015

Int J Syst Assur Eng Manag

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DEA is a non-parametric technique for measuring the relative technical efficiencies of similar decision making units (DMUs) with multiple inputs and multiple outputs. In smoe real life situations, DMUs may perform different types of functions and can be segregated into independent components such that each component has its own set of inputs and outputs. Owing the importance of the internal structure of the DMUs, DEA has been extended to multi-component DEA (MC-DEA) which is a technique for measuring the technical efficiencies of the DMUs and their components. The conventional DEA and MC-DEA approaches require crisp input and output data which may not always be available precisely. However, in real life problems, data might be imprecise or vague which can suitably be represented by fuzzy numbers. Many researchers have proposed methods to deal with fuzzy parameters in DEA and MC-DEA. However, in real situations, the decision variables can also take fuzzy forms. Therefore, the aim of the present paper is fourfold: (i) to extend DEA in the presence of undesirable outputs to fully fuzzy DEA (FFDEA) for measuring the fuzzy technical efficiencies of the DMUs in fully fuzzy environments where all decision variables and parameters are taken as fuzzy numbers, in particular triangular fuzzy numbers, (ii) to extend MC-DEA in the presence of undesirable outputs to multi-component fully fuzzy DEA (MC-FFDEA) for measuring the fuzzy technical efficiencies of DMUs and their components in fully fuzzy environments, (iii) to propose a new ranking function approach to transform both the FFDEA and MC-FFDEA models into crisp linear programming problems, and (iv) to present numerical examples in order to validate the effectiveness and advantages of the proposed approach over the existing ones.

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Section: Introductionmentioning

confidence: 87%

A fully fuzzy approach to DEA and multi-component DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs

Puri

Yadav

2015

Int J Syst Assur Eng Manag

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“…To consider undesirable outputs, Li [27] proposed model construction method for resource allocation considering undesirable outputs between different decision-making units based on the DEA framework under the CRS and VRS assumption. As well as Shiraz [55] introduced fuzzy rough data envelopment analysis (FRDEA) which apparently provide a way to accommodate the uncertainty.…”

Section: B Focus and Content Of The Publicationsmentioning

confidence: 99%

A review of Data Development Analysis (DEA) applications in supply chain management research

Chaowarat

Piboonrugnroj

Shi

2013

2013 IEEE International Conference on Industrial Engineering and Engineering Management

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The objective of this paper is to review the application of Data Envelopment Analysis (DEA) in Supply Chain Management (SCM) research including different perspectives and research topics. In this review, academic databases used were ScienceDirect, Scopus and Google Scholar. The latter database was included to identify unpublished studies, conference proceedings and other types of unpublished studies. Practical review criteria are used for the inclusion or exclusion of the pertinent literature. In total 55 publications were found and analyzed in this paper. We found that even though both DEA and SCM were established as a field of study for a long time but there are only a few that applied the DEA to SCM. Most of studies applying DEA in SCM did not consider the whole supply chain, only some parts of the chain were analyzed. Based on the findings of the review, potential research agendas were developed.

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“…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

Self Cite

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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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Fuzzy rough DEA model: A possibility and expected value approaches

Cited by 44 publications

References 33 publications

A fully fuzzy approach to DEA and multi-component DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs

A fully fuzzy approach to DEA and multi-component DEA for measuring fuzzy technical efficiencies in the presence of undesirable outputs

A review of Data Development Analysis (DEA) applications in supply chain management research

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

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