Extended Data Envelopment Analysis/Discriminant Analysis (DEA/DA), is a widely applied approach for firstly the classification of observations into groups and secondly the prediction of the membership of newly examined observations. However in real-life problems we usually face observations that are vague or can’t be precisely measured. To provide an analyst for dealing with such data, we have ...

In this paper, we propose a hybrid fuzzy logic-genetic algorithm for optimising a non-linear problem related to pressure vessel design. The fuzzy non-linear program that we obtain is solved using a Genetic Algorithm (GA), and a simulation was used for generating the initial population. The efficiency of some of the design optimisation algorithms are compared with the proposed approach of this s...

In this study, both optimistic and pessimistic approaches of data envelopment analysis are applied to propose an equitable ranking method in fuzzy environments. To this end, we suppose that the sum of efficiency scores of all decision making units (DMUs) equals to unity. Using the worst-best and best-worst approaches, the minimum and maximum possible efficiency scores of each DMU are estimated ...

The conventional data envelopment analysis (DEA) measures the relative efficiencies of a set of decision making units (DMUs) with exact value of inputs and outputs. For imprecise data, i.e., mixtures of interval data and ordinal data, some methods have been developed to calculate the interval of the efficiency scores. This paper constructs a procedure to measure the efficiencies of DMUs with mi...

Data envelopment analysis (DEA) is a linear programming problem approach for evaluating the relative efficiency of peer decision making units (DMUs) that have multiple inputs and outputs. DMUs can have a two-stage structure where all the outputs from the first stage are the only inputs to the second stage, in addition to the inputs to the first stage and the outputs from the second stage. The o...

Purpose – The purpose of this paper is to consider the following problem; if the manager of the parallel network systems wants to add new sub-decision making units (sub-DMUs) to each parallel network system, he/she wants to know how much new fuzzy inputs allocate to new sub-DMUs and how much outputs these new sub-DMUs produce such that the efficiency of each parallel network system improve or p...

this paper introduces discretionary imprecise data in data envelopment analysis (dea) and discusses the efficiency evaluation of decision making units (dmus) with non-discretionary imprecise data. then, suggests a method for evaluation the efficiency of dmus with non-discretionary imprecise data. when some inputs and outputs are imprecise and non-discretionary, the dea model becomes non-linear ...

in developed countries, improvement has been made in service providing industries along with the improvements made in quality assurance and management skills and systems in the domain of manufacturing industries. unfortunately, in developing countries, primary needs have led these countries to focus their attention illogically and solely on production and they disregard the quality of services....

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. Crisp input and output data are fundamentally indispensable in conventional DEA. However, the observed values of the input and output data in real-world problems are sometimes imprecise or vague. Many researchers ...

In this paper, we show that inverse Data Envelopment Analysis (DEA) models can be used to estimate output with fuzzy data for a Decision Making Unit (DMU) when some or all inputs are increased and deficiency level of the unit remains unchanged.