Related papers: Benchmarking within a DEA framework: setting the c…
Data Envelopment Analysis (DEA) is extended to the evaluation of performance of organizations within the framework of the implementation of plans for improvements that set management goals. Managers usually set goals without having any…
In benchmarking, organizations look outward to examine others' performance in their industry or sector. Often, they can learn from the best practices of some of them and improve. In order to develop this idea within the framework of Data…
The models that set the closest targets have made an important contribution to DEA as tool for the best-practice benchmarking of decision making units (DMUs). These models may help defining plans for improvement that require less effort…
Incentive plans involve payments for performance relative to some set of goals. In this paper, we extend Data Envelopment Analysis (DEA) to the evaluation of performance in the specific context of pay-for-performance incentive plans. The…
Natural language models are often summarized through a high-dimensional set of descriptive metrics including training corpus size, training time, the number of trainable parameters, inference times, and evaluation statistics that assess…
Data Envelopment Analysis (DEA) appears more than just an instrument of measurement. DEA models can be seen as a mathematical structure for democratic voicing within decisional contexts. Such an important aspect of DEA is enhanced through…
In a context of global economy, addressing SMEs performance within a local framework appears rather a naive approach. The key drawback of such an approach stems from its restriction to socio-economic factors that might lead to biased…
Decision-making units (DMUs) in a group convert the same resources (i.e., input indices) into the same products (i.e., output indices) at different scales. Performance indices have different measurement units, and their market prices per…
Assessing the technical efficiency of a set of observations requires that the associated data composed of inputs and outputs are perfectly known. If this is not the case, then biased estimates will likely be obtained. Data Envelopment…
This article presents a Benchmarking methodology to support decision-making for international market selection (IMS). In order to do so, we will be using an output-oriented Data Envelopment Analysis (DEA) model. This methodology considers…
The banking industry is very important for an economic cycle of each country and provides some quality of services for us. With the advancement in technology and rapidly increasing of the complexity of the business environment, it has…
Data Envelopment Analysis (DEA) is a nonparametric, data driven technique used to perform relative performance analysis among a group of comparable decision making units (DMUs). Efficiency is assessed by comparing input and output data for…
This paper proposes an integrative approach to feature (input and output) selection in Data Envelopment Analysis (DEA). The DEA model is enriched with zero-one decision variables modelling the selection of features, yielding a Mixed Integer…
Data envelopment analysis (DEA) works like a black box that does not provide any adequate detail to identify the specific reason for inefficiency in decision-making units (DMUs). The motivation of this study is to analyze the cause of the…
An alternative approach for the panel second stage of data envelopment analysis (DEA) is presented in this paper. Instead of efficiency scores, we propose to model rankings in the second stage using a dynamic ranking model in the…
This paper introduces a new specification for the nonparametric production-frontier based on Data Envelopment Analysis (DEA) when dealing with decision-making units whose economic performances are correlated with those of the neighbors…
Empirical and LLM-based research in model-driven engineering increasingly relies on datasets of software models, for instance, to train or evaluate machine learning techniques for modeling support. These datasets have a significant impact…
In this paper, a novel feature selection method is presented, which is based on Class-Separability (CS) strategy and Data Envelopment Analysis (DEA). To better capture the relationship between features and the class, class labels are…
DevOps is a trend towards a tighter integration between development (Dev) and operations (Ops) teams. The need for such an integration is driven by the requirement to continuously adapt enterprise applications (EAs) to changes in the…
Several methods have been proposed to identify which sensor sets are optimal for finding and localizing faults under different conditions for mechanical equipment. In order to preserve acceptable performance while minimizing costs, it is…