Data envelopment analysis (DEA) is a well-Known method in efficiency evaluation of a set of decision making units (DMUs) such as organizations and banks. An advantage of DEA technique is selection of weights at random. Weight selection is of crucial importance in efficiency evaluation. In this regard, it is important to employ models that have more freedom in selecting weights. One such model i...

In this paper, we propose to explore possible benefits of communication and coordination tools in open source projects using data envelopment analysis (DEA), a general method for efficiency comparisons. DEA offers several advantages: It is a non-parametric optimization method without any need for the user to define any relations between different factors or a production function, can account fo...

Data envelopment analysis (DEA) is a nonparametric approach to estimate relative efficiency of Decision Making Units (DMUs). DEA is one of the best quantitative approaches and balanced scorecard (BSC) is one of the best qualitative methods to measure efficiency of an organization. Since simultaneous evaluation of network performance of the quad areas of BSC model is considered as a necessity an...

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper introduces a neural network backpropagation Data Envelopment Analysis. Neural network requirements of computer memo...

This paper uses both the non-parametric method of Data Envelopment Analysis (DEA) and the econometric method of Stochastic Frontier Analysis (SFA) to study the production technology and cost efficiency of the U.S. dental care industry using practice level data. The American Dental Association (ADA) 2006 survey data for a number of general dental practices in Colorado are used for the empirical ...

HLP (hub location problem) tries to find locations of hub facilities and assignment of nodes to extended facilities. Hubs are facilities to collect, arrange, and distribute commodities in telecommunication networks, cargo delivery systems, etc. Hubs are very crucial and their inaccessibility impresses on network whole levels. In this paper, first, total reliability of the network is defined bas...

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...

This paper proposes an innovative procedure of finding efficient facility location–allocation (FLA) schemes, integrating data envelopment analysis (DEA) and a multi-objective programming (MOP) model methodology. FLA decisions provide a basic foundation for designing efficient supply chain network in many practical applications. The procedure proposed in this paper would be applied to the FLA pr...

Chiou et al. (2010) (A joint measurement of efficiency and effectiveness for non-storable commodities: integrated data envelopment analysis approache s. European Journal of Operationa l Research 201, 477– 489) propose an integrated data envelopment analysis model in measuring decision making units (DMUs) that have a two-stage internal netwo rk structure with multiple inputs, outputs, and consum...

This paper proposes the exchange market algorithm (EMA) to solve the combined economic and emission dispatch (CEED) problems in thermal power plants. The EMA is a new, robust and efficient algorithm to exploit the global optimum point in optimization problems. Existence of two seeking operators in EMA provides a high ability in exploiting global optimum point. In order to show the capabilities ...