Data envelopment analysis (DEA) is amathematical programmingapproach for evaluating the relative efficiency of decision making units (DMUs) in organizations. The conventionalDEAmethods require accuratemeasurement of both the inputs and outputs. However, the observed values of the input and output data in real-world problems are often imprecise or vague. Fuzzy set theory is widely used to quanti...

The aim of this study is to develop a novel decision support system, which has never been developed yet, in order optimize machining parameters. We combine the three distinct methods: experimental design and analysis, fuzzy data envelopment analysis (DEA) analytical hierarchy process (AHP). Firstly, full factorial experiment including four factors levels carried out. take into account cutting s...

data envelopment analysis (dea) has been proven as an efficient technique to evaluate the performance of homogeneous decision making units (dmus) where multiple inputs and outputs exist. in the conventional applications of dea, the data are considered as specific numerical values with explicit designation of being an input or output. however, the observed values of the data are sometimes imprec...

Performance assessment often has to be conducted under uncertainty. This paper proposes a ‘‘fuzzy expected value approach’’ for data envelopment analysis (DEA) in which fuzzy inputs and fuzzy outputs are first weighted, respectively, and their expected values then used to measure the optimistic and pessimistic efficiencies of decision making units (DMUs) in fuzzy environments. The two efficienc...

The effort in this project has been directed to studying the state-of-the-art of Data Envelopment Analysis, cooperative games, and auctions and developing corresponding models to capture vague and uncertain factors and demonstrate their potential for establishing collaboration and contracts in the increasingly global softgoods supply chain. Prototype software packages have also been developed. ...

Supplier selection is a multi-Criteria problem. This study proposes a hybrid model for supporting the suppliers’ selection and ranking. This research is a two-stage model designed to fully rank the suppliers where each supplier has multiple Inputs and Outputs. First, the supplier evaluation problem is formulated by Data Envelopment Analysis (DEA), since the regarded decision deals with uncertai...

In many real applications, the data of production processes can't be precisely measured.We develop some fuzzy versions of the classical DEA models (in particular, the CCRmodel) by using some ranking methods based on the comparison of cuts. Our approachescan be seen as an extension of the DEA methodology. The provides users and practitionerswith models which represent some real life processes mo...

Traditional Data Envelopment Analysis (DEA) models evaluate the efficiency of decision making units (DMUs) with common crisp input and output data. However, the data in real applications are often imprecise or ambiguous. This paper transforms fuzzy fractional DEA model constructed using fuzzy arithmetic, into the conventional crisp model. This transformation is performed considering the goal pr...