Search

Abstract: One of the primary concerns on target setting the electricity distribution companies is the uncertainty on input/output data. In this paper, an interactive robust data envelopment analysis (IRDEA) model is proposed to determine the input and output target values of electricity distribution companies with considering the existence perturbation in data. Target setting is implemented with the uncertain data and the decision maker (DM) can search the envelop frontier and find the targets based on his preference. In order to search the envelop frontier, the paper combine the DEA and multi-objective linear programming method such as STEM. The proposed method of this paper is capable of handling uncertainty in data and finding the target values according to the DM’s preferences. To illustrate ability the proposed model, a numerical example is solved. Also, the input and output target values for some of the electricity distribution companies in Iran are reported. The results indicate that the IRDEA model is suitable for target setting based on DM’s preferences and with considering uncertain data. [Copyright &y& Elsevier]

Abstract: Information technology (IT) is a tool crucial for enterprises to achieve a competitive advantage and organizational innovation. A critical aspect of IT management is the decision whereby the best set of IT projects is selected from many competing proposals. The optimal selection process is a significant strategic resource allocation decision that can engage an organization in substantial long-term commitments. However, making such decisions is difficult because there are lots of quantitative and qualitative factors to be considered in evaluation process. This paper has two main contributions. Firstly, it combines two well-established managerial methodologies, balanced scorecard (BSC) and data envelopment analysis (DEA), and proposes a new approach for IT project selection. This approach uses BSC as a comprehensive framework for defining IT projects evaluation criteria and uses DEA as a nonparametric technique for ranking IT projects. Secondly, this paper introduces a new integrated DEA model which identifies most efficient IT project by considering cardinal and ordinal data. Applicability of proposed approach is illustrated by using real world data of Iran Ministry of Science, Research and Technology. [Copyright &y& Elsevier]

Abstract: In recent years, BOT approach has provided an increasingly popular project financing to move toward infrastructure development in Asian countries such as Iran. There are many complexities in projects because of the variety of factors in project’s trend and also the dependence of project on mainly national factors. Due to these complexities and their long-term operating, the projects meet with uncertainty and numerous risks. In this paper, an attempt is made at identifying common risks in BOT projects. A novel hierarchical structure of risks is presented on the basis of the project-oriented point of view; next, some effective criteria for risk ranking in BOT projects are introduced. Then, the problem is defined in Fuzzy Multi Attribute Decision Making (FMADM) field. Therefore, Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) and Fuzzy Linear Programming Technique for Multidimensional Analysis of Preference (FLINMAP) methods are presented in order to rank high risks in BOT projects. In this study, these two methods are compared in four respects: separation among alternatives, fuzzy error in criteria’s weights, risk response planning, and numerousness of alternatives in proportion to criteria. The proposed model is used for identifying and assessing risks in Iran BOT power plant project. Finally, the rankings of high risks are determined according to their results at the end of this paper. [Copyright &y& Elsevier]

• We propose a novel multi-criteria data envelopment analysis model. • The proposed two-stage DEA is first considered in the presence of ratio data. • Three states are considered (black-box, free-link, and fix-link). • A multi-criteria structure is used to demonstrate the validity of the models proposed. • An illustrative example on the efficiency of 60 sets of bank branches in Iran is provided. Data Envelopment Analysis (DEA) is a well-known technique for assessing efficiency levels of decision-making units (DMUs). Very often, available data may be expressed as ratios and, in such cases, traditional DEA models cannot be applied as long as biased efficiency results are produced, yielding the issues of efficiency underestimation and pseudo-inefficiency. In this paper, a novel two-stage MCDEA-R model to handle ratio data is developed observing three distinct assumptions – black-box, free-link, and fixed-link – offering a multi-criteria decision making (MCDM) perspective to the efficiency assessment problem in productive networks. While the proposed models are tested by evaluating the efficiency levels of a set of 30 bank branches in Iran, their distinctive features are highlighted in terms of previous literature to model ratio data under network structures. Precisely, there were not only gains in terms of mitigating pseudo-inefficiency and lack of discrimination power of weights issues, but there were also actual gains in terms of efficiency reliability as measured by information entropy. [ABSTRACT FROM AUTHOR]