Data envelopment analysis (DEA) is a nonstochastic and nonparametric linear programming technique where a set of units are evaluated according to their input consumption and output production (Charnes et al. (1978)). Given that DEA efficiency analysis can be influenced by the presence of outliers, Banker and Chang (2006) proposed a method to detect atypical units through the super-efficiency mo...

-Organizations need to work on productivity improvement in order to enhance their operational performances. A number of works have taken efforts in efficiency evaluation and ranking using analytic methods such as the data envelopment analysis (DEA). However, few works have highlighted the relationship among significant factors that affect productivity. Performing efficiency evaluation and ranki...

Multiple attribute decision making (MADM) addresses the problem of choosing an optimum choice containing the highest degree of satisfaction from a set of alternatives which are characterized in terms of their attributes. In order to make a decision or choose a best alternative, a decision maker (DM) is often asked to provide his/her preferences either on alternatives or on the relative weights ...

for efficiency evaluation of some of the decision making units that have uncertain information, rough data envelopment analysis technique is used, which is derived from rough set theorem and data envelopment analysis (dea). in some situations rough data alter nonradially. to this end, this paper proposes additive rough–dea model and illustrates the proposed model by a numerical example.

This study proposes the design of a multi-attribute-decision-support-system that combines the analytical power of two different tools: data envelopment analysis (DEA) and particle swarm optimization (PSO), one of the major algorithms using swarm intelligence. DEA evaluates and measures the relative efficiency of decision making units that use multiple inputs and outputs to provide non-objective...

In this paper using DEA technique, four-stage DEA model mooted in order to consider the situation in which returned products exist. Returned products are the inputs of the previous sub-processes that need to be reprocessed due to existence of flaws. Here, a supply chain with four stages as supplier, manufacture, distributor, and retailer with intermediate and return products has been considered...

The outcome of a production process might not only deviate from a theoretical maximum due to inefficiency, but also because of non-controllable influences. This raises the issue of reliability of Data Envelopment Analysis in noisy environments. I propose to assume an i.i.d. data generating process with bounded noise component, so that the following approach is feasible: Use DEA to estimate a ps...

An algorithm stral:egy is proposed for use with the assurance region (AR) approach in data envelopment analysis (DEA). The strategy addressed in this study characterizes and cla.5sifies all decision making units (DMUs) into several subsets, using the revised simplex method of linear programming. Then, each DMU subset is solved by a different algorithm. Experimental studies consisting of randoml...

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). Recently network DEA models been developed to examine the efficiency of DMUs with internal structures. The internal network structures range from a simple two-stage process to a complex system where multiple divisions are linked together with intermediate measures. In general, there ar...

Data envelopment analysis (DEA) is a non-parametric technique to measure the efficiencies of a set of decision making units (DMUs) with common crisp inputs and outputs. In real-world problems, however, inputs and outputs typically have some levels of fuzziness. To analyze a DMU with fuzzy input/output data, previous studies provided the fuzzy DEA (FDEA) model and proposed an associated evaluati...