Browsing by Author "Ignatius, J."

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    Carbon efficiency evaluation: An analytical framework using fuzzy DEA
    (Elsevier, 2016-02-18) Ignatius, J.; Ghasemi, M. R.; Zhang, F.; Emrouznejad, A.; Hatami-Marbini, A.
    Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.
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    A Fully Fuzzified Two-stage DEA
    (2014) Hatami-Marbini, A.; Ebrahimnejad, A.; Ignatius, J.
    There is a growing literature in DEA where researchers are opening the black box to evaluate the efficiency performance of internal processes. Within this context, we study a relational two-stage DEA model where there is imprecision in the representation of the input and output data. The imprecision is captured using fuzzy sets and the models are evaluated with this representation. Insights are provided and future research directions are outlined.
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    A fuzzy decision support system for credit scoring
    (Springer, 2016-09-26) Ignatius, J.; Hatami-Marbini, A.; Rahman, A.; Dhamotharan, L.; Khoshnevis, P.
    Credit score is a creditworthiness index, which enables the lender (bank and credit card companies) to evaluate its own risk exposure toward a particular potential customer. There are several credit scoring methods available in the literature, but one that is widely used is the FICO method. This method provides a score ranging from 300 to 850 as a fast filter for high-volume complex credit decisions. However, it falls short in the aspect of a decision support system where revised scoring can be achieved to reflect the borrower’s strength and weakness in each scoring dimension, as well as the possible trade-offs made to maintain one’s lending risk. Hence, this study discusses and develops a decision support tool for credit score model based on multi-criteria decision-making principles. In the proposed methodology, criteria weights are generated by fuzzy AHP. Fuzzy linguistic theory is applied in AHP to describe the uncertainties and vagueness arising from human subjectivity in decision making. Finally, drawing from the risk distance function, TOPSIS is used to rank the alternatives based on the least risk exposure. A sensitivity analysis is also demonstrated by the proposed fuzzy AHP-TOPSIS method.
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    A fuzzy expected value approach under generalized data envelopment analysis
    (Elsevier, 2015-07-09) Ghasemi, M. R.; Ignatius, J.; Lozano, S.; Emrouznejad, A.; Hatami-Marbini, A.
    Fuzzy data envelopment analysis (DEA) models emerge as another class of DEA models to account for imprecise inputs and outputs for decision making units (DMUs). Although several approaches for solving fuzzy DEA models have been developed, there are some drawbacks, ranging from the inability to provide satisfactory discrimination power to simplistic numerical examples that handles only triangular fuzzy numbers or symmetrical fuzzy numbers. To address these drawbacks, this paper proposes using the concept of expected value in generalized DEA (GDEA) model. This allows the unification of three models – fuzzy expected CCR, fuzzy expected BCC, and fuzzy expected FDH models – and the ability of these models to handle both symmetrical and asymmetrical fuzzy numbers. We also explored the role of fuzzy GDEA model as a ranking method and compared it to existing super-efficiency evaluation models. Our proposed model is always feasible, while infeasibility problems remain in certain cases under existing super-efficiency models. In order to illustrate the performance of the proposed method, it is first tested using two established numerical examples and compared with the results obtained from alternative methods. A third example on energy dependency among 23 European Union (EU) member countries is further used to validate and describe the efficacy of our approach under asymmetric fuzzy numbers.