Prioritization method for frontier DMUs: a distance-based approach.
A DEA model for two-stage parallel-series production processes
Data envelopment analysis (DEA) has been widely used to measure the performance of the operational units that convert multiple inputs into multiple outputs. In many real world scenarios, there are systems that have a two-stage network process with shared inputs used in both stages of productions. In this paper, the problem of evaluating the efficiency of a set of specialized and interdependent components that make up a large DMU is considered. In these processes the first stage consists of two parallel...
Flexible measures in production process: A DEA-based approach
Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units () with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some and output role for others. Cook and Zhu [ (2007) 692–699] referred to these variables as flexible...
Flexible measures in production process: A DEA-based approach
Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units () with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some and output role for others. Cook and Zhu [ (2007) 692–699] referred to these variables as flexible...
Stochastic performance measurement in two-stage network processes: A data envelopment analysis approach
In classic data envelopment analysis models, two-stage network structures are studied in cases in which the input/output data set are deterministic. In many real applications, however, we face uncertainty. This paper proposes a two-stage network DEA model when the input/output data are stochastic. A stochastic two-stage network DEA model is formulated based on the chance-constrained programming. Linearization techniques and the assumption of single underlying factor of the data are used to construct...
Production planning in data envelopment analysis without explicit inputs
In the performance measurement using tools such as data envelopment analysis (DEA), data without explicit inputs has attracted considerable attention among researchers. In such studies the problem of production planning in the next production season is an important and interesting subject. Because of the uncertain nature of the future, decision makers need to provide robust procedures in order to examine alternative courses of action and their implications. The purpose of this paper is to develop...
Efficiency evaluation of closed-loop supply chains with proportional dual-role measures
Data Envelopment Analysis (DEA) is a beneficial mathematical programming method to measure relative efficiencies. In conventional DEA models, Decision Making Units (DMUs) are usually considered as black boxes. Also, the efficiency of DMUs is evaluated in the presence of the specified inputs and outputs. Nevertheless, in real-world applications, there are situations in which the performance of multi-stage processes like supply chains with forward and reverse flows must be measured such that some...
Page 1