Entropy-based air quality monitoring network optimization using NINP and Bayesian maximum entropy.

Effectual air quality monitoring network (AQMN) design plays a prominent role in environmental engineering. An optimal AQMN design should consider stations' mutual information and system uncertainties for effectiveness. This study develops a novel optimization model using a non-dominated sorting genetic algorithm II (NSGA-II).

A dataset of healthcare systems for cross-efficiency evaluation in the presence of flexible measure.

This article presents the dataset of the healthcare systems indicators of 120 countries during 2010-2017, which is related to the research article "Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems" [1]. The data is collected from the World Bank and selected for the 120 countries. Depending on their role in the performance of the healthcare systems, the indicators are categorized into input (I), output (O) and flexible measure (FM) where the FM measure can play either role of input or output in the healthcare system.

Cross-efficiency evaluation in the presence of flexible measures with an application to healthcare systems.

In recent years, most countries around the world have struggled with the consequences of budget cuts in health expenditure, obliging them to utilize their resources efficiently. In this context, performance evaluation facilitates the decision-making process in improving the efficiency of the healthcare system. However, the performance evaluation of many sectors, including the healthcare systems, is, on the one hand, a challenging issue and on the other hand a useful tool for decision- making with the aim of optimizing the use of resources.

A dataset of European banks in performance evaluation under uncertainty.

The dataset contains financial indicators from the financial statements of 250 banks operating in Europe which are collated for the 2015 accounting year. First, the dataset is split into input and outputs measures. Then the preferred number of inputs and outputs in relation to the total number of data is selected according to the rule of thumb in data envelopment analysis (DEA).