data envelopment analysis (dea) is a branch of management, concerned with evaluating the performances of homogeneous decision making units (dmus). the performances of dmus are affected by the amount of sources that dmus used. usually increases in inputs cause increases in outputs. however, there are situations where increase in one or more inputs generate a reduction in one or more outputs. in ...

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...

The inverse Data Envelopment Analysis (InvDEA) is an exciting and significant topic in the DEA area. Also, uncertain data various real-life applications can degrade efficiency results. current work addresses InvDEA presence of stochastic data. Under maintaining score, inputs/outputs-estimation problem investigated when some or all its outputs/inputs increase. A novel optimality concept for mult...

occupational accidents severely deteriorate human capital, and hence negatively affect the productivity andcompetitiveness. but despite these negative points, there are still deficiencies in safety managementperformance and indicators. therefore the safety issue needs an active management. for this reason, thewriter has proposed an approach to evaluate organizations based on safety management. ...

inthis article we offer a method of ranking contractors by using dea based onanalysis deficit and ahp. the process of hierarchical analysis (ahp) byproviding scales from paired comparison matrix, performs the contractor’sprioritizing choice. but ahp has some problems and to solve those problems,jahanshahloo and his colleagues presented a new model which uses dea andstandard deviation. in this a...

the paper deals with data envelopment analysis (dea) and artificial neural network (ann). we believe that solving for the dea efficiency measure, simultaneously with neural network model, provides a promising rich approach to optimal solution. in this paper, a new neural network model is used to estimate the inefficiency of dmus in large datasets.

the objective of this study is to determine factors affecting the technical efficiency of the inshore fisheries in kuala terengganu. data for the study was collected from a survey conducted between june and august 2007 where 100 fishermen in 14 villages were chosen by stratified sampling. data envelopment analysis (dea) and tobit analysis were employed to determine the technical efficiency leve...

this paper aims at providing a new model based on data envelopment analysis (dea) to prioritize project risks. it is clear that the large amounts of involved capitals, the long term of infrastructure projects’ implementation, and the project management problems in on-time completion of projects indicate the necessity of paying particular attention to this issue and conducting applied research i...

the basic models of data envelopment analysis (dea) are designed in such a way that the values of input and output indicators should be identified and known in them. in other words, these models are not used to consider inaccurate, interval, fuzzy, judgment data. in this paper, the aim is to not only review the past researches about the efficiency of the units by interval data and represent the...

In models of data envelopment analysis (DEA), an optimal set of weights is generally assumed to represent the assessed decision making unit (DMU) in the best light in comparison to all the other DMUs, and so there is an optimal set of weights corresponding to each DMU. The present paper, proposes a three stage method to determine one common set of weights for decision making units. Then, we use...