Data Envelopment Analysis (DEA) is a powerful tool for measuring the relative efficiency for a set of Decision Making Units (DMUs) that transform multiple inputs into multiple outputs. In centralized decisionmaking systems, management normally imposes common resource constraints to maximize operating revenues and minimize operating expenses. In this study, we propose an alternative DEA model fo...

MCDA in general and multiple objective linear programming (MOLP) in particular can be used for planning future performances. The model structures of DEA and MOLP have much in common and research on integrating DEA and MOLP has attracted increasing attentions to support both past performance assessment and future target setting in integrated manners (Cooper, 2004). For instance, Golany (1988) de...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (DMUs) with multiple inputs and multiple outputs. The original DEA models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...

DEA as an effective approach to evaluate the relative efficiency of DMUs, recently applied in many production and operation systems. This paper proposed a new k-stage DEA based MOLP model for target setting in the serial network structures, considering undesirable outputs. Since the proposed model takes the size of DMUs into account, the new idea ensures the possibility of the results. The resu...

Benchmarking methods, and in particular Data Envelopment Analysis (DEA), have become wellestablished and informative tools for economic regulation. DEA is now routinely used by European regulators to set reasonable revenue caps for energy transmission and distribution system operators. The application of benchmarking in regulation, however, requires specific steps in terms of data validation, m...

data envelopment analysis (dea) is a non-parametric method for evaluating the relative technical efficiency for each member of a set of peer decision making units (dmus) with multiple inputs and multiple outputs. the original dea models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which interval scale data can appe...

OBJECTIVE To assess prospectively the effect of benchmarking on quality of primary care for patients with type 2 diabetes by using three major modifiable cardiovascular risk factors as critical quality indicators. RESEARCH DESIGN AND METHODS Primary care physicians treating patients with type 2 diabetes in six European countries were randomized to give standard care (control group) or standar...

This paper deals with Data Envelopment Analysis (DEA) in centralised settings which the operating units belong to same organisation. In such a scenario, global system-wide perspective may be adopted as regards resource allocation and target setting. this paper, new Lexicographic Goal Programming (lexGP) approach is proposed using three priority levels: aggregated input consumption output produc...

Data envelopment analysis (DEA) is a performance measurement tool that was initially developed without consideration of the decision maker (DM)’s preference structures. Ever since, there has been a wide literature incorporating DEA with value judgements such as the goal and target setting models. However, most of these models require prior judgements on target or weight setting. This paper will...