A Step by Step Method to Improve the Performance of Decision Making Units

In this paper, we present the concept of context-dependent DEA based on the FDH model by introducing the FDH-attractiveness and FDH-progress for each DMU. By the presented method, not only we can improve the performance of inefficient DMUs, but we can find a target for improvement among the existing efficient DMUs. These targets are observed DMUs and are not some virtual points on the efficiency frontier. Also, the paper presents a step by step method to improve the performance of DMUs by measuring FDH-attractiveness and FDH-progress. One numerical example and a case study consists of 20 Iranian bank branches are given for illustration

An Improvement on STEM Method in Multi-Criteria Analysis

Multi-criteria decision making (MCDM) refers to making decision in the presence of multiple and conflicting criteria. Multiobjective programming method such as multiple objective linear programming (MOLP) are techniques used to solve such multiple criteria decision making (MCDM) problems. One of the first interactive procedures to solve MOLP is step method (STEM). In this paper we try to improve STEM method by introducing the weight vector of objectives which emphasize that more important objectives be more closer to ideal one. Therefore the presented method try to increase the rate of satisfactoriness of the obtained solution. Finally, a numerical example for illustration of the new method is given to clarify the main results developed in this pap

Using goal programming method to solve DEA problems with value judgments

Data envelopment analysis (DEA) is a linear programming approach for measuring relative efficiency of peer decision making units that have multiple inputs and outputs. DEA was developed without consideration of the decision maker’s preference structures. DEA and multiple objective linear programming are tools that can be used in management control and planning. This paper shows how a data envelopment analysis problem can be solved by transforming it into MOLP formulation. We use the goal programming method to reflect the decision making preferences in the process of assessing efficiency, such that the value judgments of the decision maker are considered. Therefore, the proposed method can find a solution that satisfies the decision maker’s goal levels. A case study is provided to illustrate how data envelopment oriented efficiency analysis can be conducted by using goal programming method

Ranking DMUs by ideal points in the presence of fuzzy and ordinal data

Envelopment Analysis (DEA) is a very effective method to evaluate the relative efficiency of decision-making units (DMUs). DEA models divided all DMUs in two categories: efficient and inefficient DMUs, and don't able to discriminant between efficient DMUs. On the other hand, the observed values of the input and output data in real-life problems are sometimes imprecise or vague, such as interval data, ordinal data and fuzzy data. This paper develops a new ranking system under the condition of constant returns to scale (CRS) in the presence of imprecise data, In other words, in this paper, we reformulate the conventional ranking method by ideal point as an imprecise data envelopment analysis (DEA) problem, and propose a novel method for ranking the DMUs when the inputs and outputs are fuzzy and/or ordinal or vary in intervals. For this purpose we convert all data into interval data. In order to convert each fuzzy number into interval data we use the nearest weighted interval approximation of fuzzy numbers by applying the weighting function and also we convert each ordinal data into interval one. By this manner we could convert all data into interval data. The numerical example illustrates the process of ranking all the DMUs in the presence of fuzzy, ordinal and interval data

A mixed ideal and anti-ideal DEA model: An application to evaluate cloud service providers

The rapid development of cloud computing and the sharp increase in the number of cloud service providers (CSPs) have resulted in many challenges in the suitability and selection of the best CSPs according to quality of service requirements. The main objective of this study is to propose three novel models based on the enhanced Russell model to increase the discrimination power in the evaluation and selection of CSPs. The proposed models are designed based on the distances to two special decisionmaking units (DMUs), namely the ideal DMU and the anti-ideal DMU. There are two advantages to the proposed ranking methods. First, they consider both pessimistic and optimistic scenarios of data envelopment analysis, so they are more equitable than methods that are based on only one of these scenarios. The second strength of this approach is its discrimination power, enabling it to provide a complete ranking for all CSPs. The proposed method can help customers to choose the most appropriate CSP while at the same time, it helps software developers to identify inefficient CSPs in order to improve their performance in the marketplace

Sustainably resilient supply chains evaluation in public transport: A fuzzy chance-constrained two-stage DEA approach

Owing to today's highly competitive market environments, substantial attention has been focused on sustainably resilient supply chains (SCs) over the last few years. Nevertheless, very few studies have focused on the efficiency evaluation analysis of the sustainability and resilience of SCs as an inevitable essential in any profitable business. This study aims to address this issue by proposing a novel fuzzy chance-constrained two-stage data envelopment analysis (DEA) model as an advanced and rigorous approach in the performance evaluation of sustainably resilient SCs. To the best of our knowledge, the current study is pioneering as it introduces a new fuzzy chance-constrained two-stage method that can be used to undertake the deterministic non-fuzzy programming of the proposed model. The proposed approach is validated and applied to evaluate a real case study including 21 major public transport providers in three megacities. The results demonstrate the advantages of the proposed approach in comparison to the existing approaches in the literature

Sesgos de memoria en el rasgo de ansiedad y en el trastorno obsesivo compulsivo

Este tema tiene gran impor-tancia práctica y teórica para comprender las diferencias individuales en la ansiedad tanto en poblaciones normales como clínicas. Existe un cre-ciente interés desde los años 70 en los factores cognitivos asociados con la ansiedad. Especialmente, se encontró que los individuos ansiosos manifiestan cierta variedad de sesgos cognitivos cuando tratan con estímulos relacionados con una amenaza (Dalgleish y Watts, 1990). Por ejemplo, las personas normales con un alto rasgo de ansiedad y los pacientes que padecen trastornos de ansiedad generalizada tienen un sesgo atencional selectivo y un ses-go de interpretación. El sesgo atencional selectivo se manifiesta por un procesamiento preferente de estímulos relacionados con la amenaza sobre los estímulos neutrales, y el sesgo de interpretación se muestra por una tendencia a interpretar los estímulos ambiguos de una forma amenazadora (Eysenck y Byrne, 1994)

An Interactive Procedure to Solve Multi-Objective Decision-Making Problem: An Improvment to STEM Method

Decisions in the real-world contexts are often made in the presence of multiple, conflicting, and incommensurate criteria. Multiobjective programming methods such as multiple objective linear programming (MOLP) are techniques used to solve such multiple-criteria decision-making (MCDM) problems. One of the first interactive procedures to solve MOLP is STEM method. In this paper we try to improve STEM method in a way that we search a point in reduced feasible region whose criterion vector is closest to positive ideal criterion vector and furthest to negative ideal criterion vector. Therefore the presented method tries to increase the rate of satisfactoriness of the obtained solution. Finally, a numerical example for illustration of the new method is given to clarify the main results developed in this paper

The Prediction of Mental Quality of Life Based on Defectiveness/Shame Schema with Mediating Role of Emotional Intelligence and Coping Strategies by Means of Structural Equations Modeling

Early maladaptive schema is assumed to be a disrupting factor for quality of life. Yet, the mechanism of this vulnerability is not well known. The purpose of this study was to investigate the characteristic of emotional intelligence and coping strategy with stress as a mediator between early maladaptive defectiveness/ shame and mental quality of life. Participants were 245 men and women in Isfahan who were selected as the sample by availability sampling method. They completed the Petrides and Furnham's Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF), Coping Inventory for stressful situation (CISS) and WHO Quality of Life-BREF (WHOQOL-BREF) and Young Schema Questionnaire-Short Form (YSQ-SF). Data was analyzed by means of structural equation modeling. The results indicated that the suggested model of study needs modification and only emotional intelligence was the mediator. Standard path coefficient of defectiveness/shame schema to emotional intelligence was -0.55 and emotional intelligence to problem focused coping, emotion focused coping and mental quality of life were 0.49, -0.59 and 0.78 (p<0.05). Based on results, emotional intelligence training can improve mental quality of life and coping strategies in people who have early defectiveness/shame maladaptive schema