Evaluating the efficiency of electricity distribution companies (EDCs) accurately is one of the most important issues for regulators and policy makers. This research combines the results of data envelopment analysis (DEA) and corrected ordinary least squares (COLS) with machine learning techniques to evaluate a set of EDCs in the period 2011–2020. We propose a three-stage process. First, for each year, the efficiency scores of EDCs are measured using DEA and COLS methods. Then, this study applies support vector regression (SVR), a powerful machine learning technique, to estimate the efficient frontier and to calculate the efficiency of the EDCs. The efficiencies generated by DEA, COLS, and SVR are not the same and are used to construct fuzzy triangular numbers. Finally, the fuzzy efficiencies are considered as criteria for the technique for order performance by similarity to the ideal solution (TOPSIS), and the final efficiencies and ranks are obtained using the fuzzy TOPSIS (FTOPSIS) method. In addition, using the fuzzy C-means clustering (FCM) algorithm, the EDCs are clustered and discussed. The results show that there are increasing and decreasing trends for the selected EDCs in the period 2011–2022. In addition, some EDCs act in a poor situation and their performance should be improved.

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis. Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking.

The problem of assessment of Decision Making Units (DMUs) by using Data Envelopment Analysis (DEA) may not be straightforward due to the data uncertainty. Several studies have been developed to incorporate uncertainty into input/output values in the DEA literature. On the other hand, while traditional DEA models focus more on crisp data, there exist many applications in which data is reported in form of intervals. This paper considers the box-uncertainty in data which means that each input/output value is selected from a symmetric box. This specific type of uncertainty has been addressed as Interval DEA approaches. Our proposed model deals with efficiency evaluation of DMUs with imprecise data in a robust optimization. We assume that inputs and outputs are reported in the form of intervals and propose the robust counterpart problem for the envelopment form of the DEA model. Further, we also develop two ranking methods which have more benefits compared to some existing approaches. An illustrative example is provided to show how the proposed approaches work. An application on hospital efficiency in East Virginia is used to show the usefulness of the proposed approaches.

An increasing number of organizations and businesses around the world use cloud computing services to improve their performance in the competitive marketplace. However, one of the biggest challenges in using cloud computing services is performance measurement and the selection of the best cloud service providers (CSPs) based on quality of service (QoS) requirements[13]. To address this shortcoming in this article we propose a network data envelopment analysis (DEA) method in measuring the efficiency of CSPs. When network dimensions are taken into consideration, a more comprehensive analysis is enabled where divisional efficiency is reflected in overall efficiency estimates. This helps managers and decision makers in organizations to make accurate decisions in selecting cloud services. In the current study, the non-oriented network slacks-based measure (SBM) model and conventional SBM model with the assumptions of constant returns to scale (CRS) and variable returns to scale (VRS) are applied to measure the performance of 18 CSPs. The obtained results show the superiority of the network DEA model and they also demonstrate that the proposed model can evaluate and rank CSPs much better than compared to traditional DEA models.

In this paper, we reformulate the conventional DEA models as an imprecise DEA problem and propose a novel method for evaluating the DMUs when the inputs and outputs are fuzzy and/or ordinal or vary in intervals. For this purpose, we convert all data into interval data. In order to convert each fuzzy number into interval data, we use the nearest weighted interval approximation of fuzzy numbers by applying the weighting function, and we convert each ordinal data into interval one. In this manner, we could convert all data into interval data. The presented models determine the interval efficiencies for DMUs. To rank DMUs based on their associated interval efficiencies, we first apply the Ω-index that is developed for ranking of interval numbers. Then, by introducing an ideal DMU, we rank efficient DMUs to present a complete ranking. Finally, we use one example to illustrate the process and one real application in health care to show the usefulness of the proposed approach. For this evaluation, we consider interval, ordinal, and fuzzy data alongside the precise data to evaluate 38 hospitals selected by OIG. The results reveal the capabilities of the presented method to deal with the imprecise data.

Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.

The continuous development of energy management systems, coupled with a growing population, and increasing energy consumption, highlights the necessity to develop a deep understanding of household energy consumption behavior and interventions that facilitate behavioral change. Using a data mining segmentation technique, 2,505 Northern Ireland households were segmented into four distinctive profiles, based on their energy consumption patterns, socio-demographic, and dwelling characteristics. The change in attitude towards energy consumption behavior was analyzed to evaluate the impact of smart meter feedback as well. The key finding was 81% of trial participants perceived smart meters to be helpful in reducing their energy consumption. In addition, we found that the potential to reduce energy bills and environmental concerns were the strongest motivations for behavior change.

The emergence of novel COVID-19 is causing an overload on public health sector and a high fatality rate. The key priority is to contain the epidemic and reduce the infection rate. It is imperative to stress on ensuring extreme social distancing of the entire population and hence slowing down the epidemic spread. So, there is a need for an efficient optimizer algorithm that can solve NP-hard in addition to applied optimization problems. This article first proposes a novel COVID-19 optimizer Algorithm (CVA) to cover almost all feasible regions of the optimization problems. We also simulate the coronavirus distribution process in several countries around the globe. Then, we model a coronavirus distribution process as an optimization problem to minimize the number of COVID-19 infected countries and hence slow down the epidemic spread. Furthermore, we propose three scenarios to solve the optimization problem using most effective factors in the distribution process. Simulation results show one of the controlling scenarios outperforms the others. Extensive simulations using several optimization schemes show that the CVA technique performs best with up to 15%, 37%, 53% and 59% increase compared with Volcano Eruption Algorithm (VEA), Gray Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), respectively.

Several network-data envelopment analysis (DEA) performance assessment models have been proposed in the literature; however, the conflicts between stages and insufficient number of decision-making units (DMUs) challenge the researchers. In this paper, a novel game-DEA model is proposed for efficiency assessment of network structure DMUs. We propose a two-stage modeling, where in the first stage network is divided into several sub-networks; we at the same time categorize input variables to measure efficiency of sub-networks within each input category. In the second stage, we calculate efficiency of the network by aggregating efficiency scores of sub-networks within each category. In this way, the issue of insufficient number of DMUs when there are many input/output variables can be handled as well. One of the main contributions of this paper is assuming each category and stage as a player in Nash bargaining game. Using the concept borrowed from Nash bargaining game model, the proposed game-DEA model tries to maximize distances of efficiency scores of each player form their corresponding breakdown points. The usefulness of the model is presented using a real case study to measure the efficiency of bank branches.

Hospitals are the main sub-section of health care systems and evaluation of hospitals is one of the most important issue for health policy makers. Data Envelopment Analysis (DEA) is a nonparametric method that has recently been used for measuring efficiency and productivity of Decision Making Units (DMUs) and commonly applied for comparison of hospitals. However, one of the important assumption in DEA is that DMUs must be homogenous. The crucial issue in hospital efficiency is that hospitals are providing different services and so may not be comparable. In this paper, we propose an integrated fuzzy clustering cooperative game DEA approach. In fact, due to the lack of homogeneity among DMUs, we first propose to use a fuzzy C-means technique to cluster the DMUs. Then we apply DEA combined with the game theory where each DMU is considered as a player, using Core and Shapley value approaches within each cluster. The procedure has successfully been applied for performances measurement of 288 hospitals in 31 provinces of Iran. Finally, since the classical DEA model is not capable to distinguish between efficient DMUs, efficient hospitals within each cluster, are ranked using combined DEA model and cooperative game approach. The results show that the Core and Shapley values are suitable for fully ranking of efficient hospitals in the healthcare systems.

Distributing loan using group lending method is one of the unique features in microfinance, as it utilises peer monitoring and dynamic incentive to lower credit risks in extending collateral-free loan to the poor. However, many microfinance institutions (MFIs) eventually perceive it to be costly and restricting loan growth thereby resorted to individual lending method to enhance profitability. On the other hand, village banking method was developed to boost outreach and to create self-sustaining village microbanks. We thus seek to empirically observe the loan method – efficiency relationship and to examine the best loan method regionally; focusing on not-for-profit MFIs that are widely regarded as best microfinance provider. Non-oriented Data Envelopment Analysis with regional meta-frontier approach is used for efficiency assessment of 628 MFIs from 87 countries in 6 regions, followed by Tobit regression. We also investigated factors affecting efficiencies such as borrowings, total donation, cost per borrower (CPB), portfolio at risk (PAR), interest rates, MFI age, regulation status, and legal format. The results support our argument that appropriate performance analysis should best be performed on regional basis separately as we find different results for different region.

This study surveys the ordered weighted averaging (OWA) operator literature using a citation network analysis. The main goals are the historical reconstruction of scientific development of the OWA field, the identification of the dominant direction of knowledge accumulation that emerged since the publication of the first OWA paper, and to discover the most active lines of research. The results suggest, as expected, that Yager's paper1 (IEEE Trans. Systems Man Cybernet, 18(1), 183–190, 1988) is the most influential paper and the starting point of all other research using OWA. Starting from his contribution, other lines of research developed and we describe them.

Abstract: Data envelopment analysis (DEA) is a non‐parametric method for measuring the efficiency and productivity of decision‐making units (DMUs). On the other hand data mining techniques allow DMUs to explore and discover meaningful, previously hidden information from large databases. Classification and regression (C&R) is the commonly used decision tree in data mining. DEA determines the efficiency scores but cannot give details of factors related to inefficiency, especially if these factors are in the form of non‐numeric variables such as operational style in the banking sector. This paper proposes a framework to combine DEA with C&R for assessing the efficiency and productivity of DMUs. The result of the combined model is a set of rules that can be used by policy makers to discover reasons behind efficient and inefficient DMUs. As a case study, we use the proposed methodology to investigate factors associated with the efficiency of the banking sector in the Gulf Cooperation Council countries.

Using panel data for 52 developed and developing countries over the period 1998-2006, this article examines the links between information and communication technology diffusion and human development. We conducted a panel regression analysis of the investments per capita in healthcare, education and information and communication technology against human development index scores. Using a quantile regression approach, our findings suggest that changes in healthcare, education and information and communication technology provision have a stronger impact on human development index scores for less developed than for highly developed countries. Furthermore, at lower levels of development education fosters development directly and also indirectly through their enhanced effects on ICT. At higher levels of development education has only an indirect effect on development through the return to ICT.