It is well established that multiple reference sets may occur for a decision making unit (DMU) in the non-radial DEA (data envelopment analysis) setting. As our first contribution, we differentiate between three types of reference set. First, we introduce the notion of unary reference set (URS) corresponding to a given projection of an evaluated DMU. The URS includes efficient DMUs that are act...

DEA (Deta Envelopment Analysis) is a non-parametric technique for measuring the efficiency of DMUs (Decision Making Units)with common inputs and outputs [2,5]. viewpoint for each DMU because of taking a maximum ratio. During recent years, the issue of sensitivity and stability of data envelopment analysis results has been extensively studied. The first DEA sensitivity analysis paper by Charnes ...

Cross-efficiency evaluation is an effective way of ranking decision making units (DMUs) in data envelopment analysis (DEA). Existing approaches for cross-efficiency evaluation are mainly focused on the calculation of cross-efficiency matrix, but pay little attention to the aggregation of the efficiencies in the cross-efficiency matrix. The most widely used approach is to aggregate the efficienc...

Data envelopment analysis is a new field of interdisciplinary research in operations management science and mathematical economics. uses programming to evaluate the relationship between decision-making units with multiple inputs outputs (abbreviated as DMU). Effectiveness (DEA effective) judge whether DMU located on "frontier surface" production possibility set. This paper proposes method for e...

sohrabi and nalchigar (2010) proposed a new data envelopment analysis (dea) model to identify the most efficient decision-making unit (dmu) in presence of imprecise data. in this paper, it is shown that the proposed model is not able to determine the most efficient dmu and is randomly introduced an efficient dmu. in addition, it is shown that this model determines the most efficient dmu in the ...

Data envelopment analysis (DEA) is a non-parametric technique for measuring and analyzing the relative efficiency of Decision-Making Unit (DMU) having multiple homogeneous types inputs outputs. Traditional DEA treats DMU as black boxes calculates their efficiencies by considering initial final But overall directly depended upon performances intermediate processes operations. In many situations,...

Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency ...

Assembly activities represent a crucial phase of new product development processes. The identification of potential assembly issues early in the design stage can thus allow companies to save time and cost of successive manufacturing phases. In such context, this article analyses the potential of digital mock-up (DMU) to enhance assembly feasibility and efficiency through specific design analysi...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

Classic data envelopment analysis (DEA) models determine the efficiency of productive units, called decision making units (DMUs). DEA uses as its methodology the equiproportional reduction of inputs or increase of outputs and the finding of a single target for each DMU. This target does not incorporate the preference of the decision maker. Later works propose obtaining alternative targets based...