We present and demonstrate a multi-criteria approach for evaluating R&D projects in different stages of their life cycle. Our approach integrates the balanced scorecard (BSC) and data envelopment analysis (DEA) and develops an extended DEA model. The input and output measures for the integrated DEA–BSC model are grouped in “cards” which are associated with a “BSC for R&D projects”. The BSC is e...

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.

• Subsampling bootstrap procedure for DEA estimator is extended to network structure The subsampling proposed also considers undesirable factors Evidence on the performance of obtained through Monte Carlo experiments method applied evaluation railways in OECD considering noise pollution problem Data Envelopment Analysis (DEA), provides an empirical estimation production frontier, based observed...

the present study is an attempt towards remodeling cost, revenue and profit relationship within the network process. the previous models of data envelopment analysis (dea) have been too general in their scope and focused on the input and the output within a black box system, therefore they have not been able to measure various phases simultaneously within a network system. by using these models...

malmquist productivity index (mpi) is a numeric index that is of great importance in measuring productivity and its changes. in recent years, tools like dea have been utilized for determining mpi. in the present paper, some models are recommended for calculating mpi when there are just ratio data available. then, using dea and dea-r, some models are proposed under the constant returns to scale ...

Data Envelopment Analysis (DEA) is an eciency measurement tool for evaluation of similar Decision Making Units (DMUs). In DEA, weights are assigned to inputs and outputs and the absolute eciency score is obtained by the ratio of weighted sum of outputs to weighted sum of inputs. In traditional DEA models, this measure is also equivalent with relative eciency score which evaluates DMUs in compar...

this paper deals with the problem of optimizing two-stage structure decision making units (dmus) where the activity and the performance of two-stage dmu in one period effect on its efficiency in the next period. to evaluate such systems the effect of activities in one period on ones in the next term must be considered. to do so, we propose a dynamic dea approach to measure the performance of su...

We propose a dynamic DEA model involving network structure in each period within the framework of a slacks-based measure approach. We have previously published the network SBM (NSBM) and the dynamic SBM (DSBM) models separately. Hence, this article is a composite of these two models. Vertically, we deal with multiple divisions connected by links of network structure within each period and, hori...