Abstract In this paper, by using Data Envelopment Analysis (DEA) technique a method is proposed to find efficient solutions of 0-1 Multiple Objective Linear Programming (MOLP) problem. In this method from a feasible solution of 0-1 MOLP problem, a Decision Making Unit (DMU) without input vector is constructed in which output vector for DMU is the values of objective functions. The method consis...

Data envelopment analysis (DEA) is a mathematical programming technique for identifying efficiency scores of decision making units (DMUs). Since DEA models cannot present efficient frontiers of PPS, in order to do this, we introduce a method for identifying efficient frontier for DMUs with interval data.

This study consists of three types of vendor selection models in supply chains and presents a decision making scheme for choosing appropriate method for supplier selection under certainty, uncertainty and probabilistic conditions. These models are, Data Envelopment Analysis (DEA), Fuzzy Data Envelopment Analysis (FDEA), and Chance Constraint Data Envelopment Analysis (CCDEA). In FDEA model we u...

Ranking decision making units (DMUs) is one of the most important applications of data envelopment analysis (DEA). In this paper, we exploit the power of individual appreciativeness in developing a methodology that combines crossevaluation, preference voting and ordered weighted averaging (OWA). We show that each stage of the proposed methodology enhances discrimination among DMUs while offerin...

one of the difficulties of data envelopment analysis(dea) is the problem of deciency discriminationamong efficient decision making units(dmus) and hence, yielding large number of dmus as efficientones. the main purpose of this paper is to overcome this inability. one of the methods for rankingefficient dmus is minimizing the coefficient of variation (cv) for inputs-outputs weights. in this pap...

in the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results. in these ‘‘deterministic’’ frontier models, statistical theory is now mostly available. this paper deals with the statistical pared sample method and its capability of detecting outliers in data envelopment analysis. in the prese...

one of the major problems in data envelopment analysis (dea) is to determine the projection of inefficient decision making units (dmus) into the efficient frontier. in conventional dea models, inputs and outputs of inefficient dmus alter arbitrarily for reaching to the efficient frontier. nevertheless, sometimes the ability of dmus is defined and restricted. moreover, there are situations in th...

Data envelopment analysis (DEA) is a method used for measuring the efficiency of decision-making units‎. Unlike the standard models‎, ‎which assume decision-making units to be a black box‎, ‎network data envelopment analysis focuses on the internal structure of these units‎. ‎Some researchers have developed a two-stage method where all the inputs are entirely used in the first stage‎, ‎producin...

This article elaborates on the applicability of basic and extended data envelopment analysis (DEA) models for various information system (IS) decision use-cases including illustrative examples from an enterprise resource planning (ERP) software investment appraisal. The usage of data envelopment analysis models and their extensions for IS decisions remains limited. This omission seems critical ...

over the years we have witnessed the growing importance of supply chain operations and supply chain management (scm), as supply chains play a major role in every step of the product life cycle. therefore, this paper will dea-based performance evaluation approaches for overall supply chain can be categorized. future perspectives and challenges are discussed