Data envelopment analysis (DEA) creates many opportunities for collaboration between analyst and decision-maker. There are, however, situations in which all of the decision-making units (DMUs) fall under the umbrella of a centralized decision maker that oversees them. Many organizations such as bank branches, chain stores, … can do this. This centralized decision maker unit expect that resource...

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision-making units (DMUs). It is particularly useful when no a priori information is available on the trade-offs or relationships among various performance measures. A shortcoming of the DEA model, however, is its inability to provide a measure of absolute performance for the DMUs under in...

There is an urgent need in a wide range of fields such as logistics and supply chain management to develop effective approaches to measure and/or optimally design a network system comprised of a set of units. Data envelopment analysis (DEA) researchers have been developing network DEA models to measure decision making units’ (DMUs’) network systems. However, to our knowledge, there are no previ...

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...

for better guiding a system, senior managers should have accurate information. using data envelopment analysis (dea) help managers in this objective. thus, many investigations have been made in order to find the most productive scale size (mpss) for the evaluating decision making units (dmus). in this paper we consider this case where there exist subsets of input and output variables to be inte...

The Malmquist index (MI) has demonstrated its usefulness in comparing the performances of Decision Making Units (DMUs) performances. global MI (GMI) been suggested as a means to overcome three drawbacks MI: non-circularity, disparate measurements, and infeasibility. Recently, it appeared that can also be used compare groups DMUs. While this new function increased usefulness, presents same MI. I...

The highest efficiency score 1 (100% efficiency) is regarded as a common benchmark for Decision Making Units (DMUs). This brings about the existence of more than one DMU with the highest score. Such a case normally occurs in all Data Envelopment Analysis (DEA) models and also in all the Common Set of Weights (CSWs) methods and it may lead to the lack of thorough ranking of DMUs. And ideal DMU b...

Allocating a fixed cost among set of peer decision-making units (DMUs) is one the most important applications data envelopment analysis. However, almost all existing studies have addressed allocation (FCA) problem within traditional framework while ignoring existence undesirable outputs. Undesirable outputs are neither scarce in various production activities real world nor trivial efficiency ev...

Literature suggests two main approaches, parametric and non-parametric, for constructing efficiency frontiers using which efficiency scores of other units can be based. Parametric functions can be either deterministic or stochastic in nature. However, when multiple inputs and outputs are encountered, Data Envelopment Analysis (DEA), a non-parametric approach, is a powerful tool used for decades...

Abstract: Performance measurement has received significant interest during the present industrial scenario. Researchers are emphasizing the development of newer exotic cutting edge tools and more effective methods that can be applied to solve organisational problems. Data Envelopment Analysis (DEA) model measure the efficiencies of a set of homogenous systems or decision making units (DMUs) by ...