Sentence examples for efficiencies of decision-making from inspiring English sources

Data envelopment analysis (DEA) is an approach to measure the relative efficiencies of decision-making units (DMUs) with multiple inputs and multiple outputs without underlying assumptions.

Hafezalkotob et al. (2015) proposed a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there were discrete uncertain input and output data.

In this paper, we propose a novel robust data envelopment model (RDEA) to investigate the efficiencies of decision-making units (DMU) when there are discrete uncertain input and output data.

Data Envelopment Analysis DEAA) is a very effective method to evaluate the relative efficiency of decision-making units (DMUs).

DEA is a linear programming technique used to evaluate the efficiency of decision-making units (DMUs) where multiple inputs and outputs are involved.

This paper uses Data Envelopment Analysis DEAa), a nonparametric approach, to evaluate the relative efficiency of decision-making units (DMUs) that use multiple inputs to produce multiple outputs, to evaluate the relative managerial efficiency of TDPs.

To accommodate multiple inputs and outputs whose relationships are unknown, this study employs data envelopment analysis (DEA), a multi-factor productivity model for measuring the relative efficiency of decision-making units without any assumption of a production function.

Data Envelopment Analysis (DEA) is the dominant non-parametric approach to evaluate the efficiency of Decision-Making Units (DMU).

Data envelopment analysis (DEA) of Charnes, Cooper and Rhodes (CCR) model was developed by Charnes et al. [8] to evaluate the efficiency of decision-making units (DMUs).

A controlled experiment aimed at evaluating the impact of design rationale documentation techniques on effectiveness and efficiency of decision-making in the presence of requirements changing is presented in (Falessi et al. 2006).

Data envelopment analysis (DEA) has emerged as an effective and popular method for evaluating the efficiency of decision-making units (DMUs) in different sectors including the health sector.