In this paper, we present a recurrent neural network model for solving CCR Model in Data Envelopment Analysis (DEA). The proposed neural network model is derived from an unconstrained minimization problem. In the theoretical aspect, it is shown that the proposed neural network is stable in the sense of Lyapunov and globally convergent to the optimal solution of CCR model. The proposed model has...

Supply chain performance evaluation problems are complex problems with multiple criteria and multi-layered internal linking activities. Data Envelopment Analysis (DEA) has been used to evaluate the relative performance of organizational units called Decision Making Units (DMUs). However, the conventional DEA models cannot take into account the complex nature of supply chains with internal linki...

Diethanolamine-Induced Alteration of Hepatic Mitochondrial Function and Structure. BARBEE, S. J., AND HARTUNG, R. (1979). Toxicol. Appl. Pharmacol. 47,431-440. Diethanolamine (DEA) has been shown to interfere with phospholipid metabolism. It was hypothesized that DEA may alter membrane-bound biochemical processes which are dependent upon phospholipids. DEA was investigated for its effect on hep...

In the conventional data envelopment analysis (DEA) internal sub-processes of the production units are ignored. The current paper develops a network-DEA super-efficiency model to compare the performance of efficient network systems. A new ranking method is developed by aggregating the computed super-efficiency scores with a J-divergence measure. The proposed approach is then applied to evaluate...

Two stages DEA models are used in many fields of management and industry. One of the concepts that has attracted the attention of researchers in the theory of production is the concept of ranking the units with a two-stage network. A unit ranking can provide useful information to decision makers (DMUs) about optimal decision making activities. This concept defines the superiority of a unit in t...

Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency ...

Since data envelopment analysis (DEA) introduced in 1970s, it has been widely applied to measure the efficiency of a wide variety of production and operation systems. Recently DEA has been extended to examine the efficiency of decision making units (DMUs) with two-stage network structures or processes, where the outputs from the first stage are intermediate measures that make up the inputs of t...