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...

Measuring the efficiency of real businesses is not a simple task, because a real business may involve several processes and sub-processes, forming a very complicated dynamic network of interactions. In this paper, a customized dynamic network data envelopment analysis (NDEA) model is proposed to measure the efficiency of the sub-processes in a real business. The proposed dynamic NDEA model is f...

This paper adopts a modified approach of data envelopment analysis (DEA) to measure the academic efficiency of university departments. In real-world case studies, conventional DEA models often identify too many decision-making units (DMUs) as efficient. This occurs when the number of DMUs under evaluation is not large enough compared to the total number of decision variables. To overcome this l...

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...

In the environment of business‐to‐business e‐commerce, Buyers and sellers in mature industrial markets can turn single transactions into long-term beneficial relationships by a deeper understanding of the complex connection between the two and buyers and sellers are uncertain about their roles. A “must-do” for the sellers, in particular, is to understand patterns of investment and reward,...

In this paper, a novel approach for multi response optimization is presented. In the proposed approach, response variables in treatments combination occur with a certain probability. Moreover, we assume that each treatment has a network style. Because of the probabilistic nature of treatment combination, the proposed approach can compute the efficiency of each treatment under the desirable reli...

The linguistic terms used to describe the qualitative data in problems are symmetrical about central word. With increasing complexity and a large number of DMs with different domain expertise involved decision-making, symmetric term set seems constricted. This paper examines multiple-criteria group decision-making which decision-makers use unbalanced 2-tuple alternative–criteria assessments mat...