Supplier selection is perceived as important decision-making process in any supply chain management. In this study, the best supplier for a company being determined based on five main criteria chosen which are delivery, capacity, warranty, cost, and quality. The Analytic Hierarchy Process (AHP) Entropy Measurement (EM) methods integrated with Data Envelopment Analysis (DEA) was applied to set r...

In this study, Data Envelopment Analysis (DEA) models are improved by employing spherical fuzzy sets (SFSs), which is an extension of generalized sets. SFSs were recently introduced as a novel type set that allows decision-makers to express their level uncertainty directly. As result, provide more preferred domain for decision-makers. Fundamental Charnes-Cooper-Rhodes (CCR) model discussed on t...

Data envelopment analysis (DEA) is a useful mathematical tool for evaluating the performance of production units and ranking their relative efficiency. In many real-world applications, belong to several separate groups also consist sub-units. this paper, we introduce new method group efficiency two-stage systems. To end, some DEA models are introduced systems based on average weakest criteria. ...

Data envelopment analysis (DEA) calculates the relative efficiency of homogenous decision-making units (DMUs) with multiple inputs and outputs. Classic DEA models usually suffer from several issues such as: discrimination power, variable weights of inputs/outputs, inaccurate efficiency estimation for small number of DMUs, incapability in working with zero and negative data, and not having exter...

  In this paper, presenting two simple methods for ranking of efficient DMUs in DEA models that included to add one virtual DMU as ideal DMU and is using the additive model. Note that, we use an ideal point just for comparing efficient DMUs with. Although these methods are simple, they have ability for ranking all efficient DMUs, extreme points and the others, also they are capable of ranking t...

All the basic models in data envelopment analysis (DEA) divide decision making units (DMUs) in two groups: efficient DMUs and inefficient DMUs, and lack of discrimination of efficient units is a serious problem. Also in spite of completely ranking units in analytical hierarchy process (AHP), the process of making pairwise comparison matrix is based on experts’ choices and it causes error and in...

in the real world there are groups which composed of independent units. the conventional data envelopment analysis(dea) model treats groups as units, ignoring the operation of individual units within each group.the current paper, investigates parallel system network approach proposed by kao and modifies it. as modied kao' model is more eligible to recognize ecient groups, a new ranking m...

Abstract The regulation of water companies (WCs) is essential to protect the interests citizens, as are natural monopolies. Consequently, several methodological approaches applied by regulators benchmark performance WCs, with data envelopment analysis (DEA) being one most widely applied. However, traditional DEA models allocate different weights variables considered in efficiency assessments ea...

The conventional paradigm in data envelopment analysis (DEA) is to develop an efficiency measurement model that assumes the input and output data are precise and equal to some nominal values. However, this paradigm does not take into consideration the inherent uncertainties in real-life performance measurement problems. As a result of these uncertainties, the input and output data may take non-...