Multi-criteria Data Envelopment Analysis Research Articles

Investigation into the efficiencies of European football clubs with bi-objective multi-criteria data envelopment analysis

A financially successful football club can achieve sporting achievements as well as become financially stable. From this point of view, in this study, the efficiencies of clubs were investigated with the Bi-Objective Multi-Criteria Data Envelopment Analysis (BiO-MCDEA) model by using financial and sporting data of the 2015-2016, 2016-2017 and 2017-2018 seasons of 10 football clubs in the Big-Five League which is the locomotive of the football industry. In the study, the number of social media followers, the average number of viewers and total market value were used as input, and the UEFA club score and total revenues were used as output. As a result, Arsenal, Paris Saint-Germain, and Juventus were determined as efficient in the 2015-2016 season, Paris Saint-Germain and Liverpool in the 2016-2017 season, Manchester United, Paris Saint-Germain and Chelsea in the 2016-2017 season. The reasons as to why Paris Saint-Germain was efficient in all three seasons were also examined. In addition, in the sensitivity analysis conducted to determine the effect of inputs and outputs on the model, it was concluded that efficiency was highly related to financial data.

A knowledge system proposal with weighted goal programming approach for multi-criteria dea model and an application

In today's competitive environment, enterprises should use their resources correctly; they should continuously improve themselves and work efficiently. It is important to evaluate the performances of the units under the same conditions in enterprises according to each other, to see the current situations and to determine appropriate improvements in necessary points. One of the commonly used approaches to performance evaluation is Data Envelopment Analysis. Many approaches have been developed for the Data Envelopment Analysis model, and Goal programming using in multi-objective decision making solutions approaches is one of them. Goal Programming gives decision-makers the opportunity to evaluate many objectives together in the decision-making process. In this study, classical Data Envelopment Analysis and weighted goal programming approach for multi-criteria data envelopment analysis model was applied in the evaluation process of the projects worked in an automotive supplier industry. A knowledge system has also been proposed in order to evaluate the effectiveness of the projects periodically and to include new projects or conditions into the evaluation.

Efficiency evaluation of Brazilian airlines operations considering the Covid-19 outbreak

In this paper we evaluate the operational efficiency of the Brazilian airlines considering the novel coronavirus Covid-19 outbreak. This novel coronavirus was first reported end of 2019 in Wuhan, China, however the powerful contamination spread among people forced the World Health Organization to characterize the Covid-19 as a pandemic in March of 2020. Here we analyze the main Brazilian airlines operations response due to lower demand because Covid-19 outbreak in first quarter of 2020 comparing with first quarter of 2019. The analysis here aims to verify the efficiency of airlines in domestic air transport market in Brazil through Multicriteria Data Envelopment Analysis (MCDEA) model. We used MCDEA to avoid limitations of classical DEA models for the case, especially the numbers of decision units and variable. In this paper we used an improvement of the MCDEA model to seek benchmarks considering a dual model all objective functions of MCDEA. The results highlight the challenges for the airlines, due to flight restriction and demand dropping. And also, the evaluation exposes the different company configuration of aircrafts age and network reconfiguration which was reflected by the efficiency difference on the period. The assessment shows the company with a better mix of aircraft models has a leverage on efficiency response due to unpredictable period as the pandemic Covid-19 outbreak.

A Multi-criteria Approach for Distribution Network Expansion Through Pooled MCDEA and Shannon Entropy

Abstract Power distribution network expansion planning (DNEP), based on innovative load flow analysis and optimization techniques, has drawn great attention of researchers around the world to cater for ever increasing demand of electrical power. In the present work, a new approach based on Multi-criteria Data Envelopment Analysis and Shannon Entropy analysis (MCDEA-SEA) is presented that strengthens the solution hunt process of DNEP problems. In the first stage of proposed methodology, various probable configurations are determined through load flow analysis considering different objective functions and constraints. In the next stage, large amount of complex data sets generated for various configurations from power flow perusal, then analyzed using pooled MCDEA-SEA. This multi-stage approach expedites search for best and impeccable solution, which leaves no space for sub-optimality. The efficacy of the proposed method has been verified by applying it on IEEE 33-bus test distribution system, considering impending load scenarios and the results show that the proposed methodology can effectively solve DNEP problem and provide optimal solution for DNEP problems, consuming lesser computation time compared to conventional approaches.

Market-oriented transmission expansion planning using non-linear programming and multi-criteria data envelopment analysis

Abstract This paper presents a novel and effective methodology for transmission expansion planning (TEP) in a double-auction based electricity market. The TEP problem has been formulated as a nonlinear programming problem to maximize the social welfare function of market participants (Generation and Distribution Companies), subject to power system operational and security constraints. The proposed methodology has been solved in two parts: in the first part, nonlinear TEP problem has been solved for a number of market scenarios to identify a set of prospective transmission lines; and in second part, multi-criteria data envelopment analysis has been applied on the identified set of prospective lines to select the most effective ones for transmission reinforcement. The stopping criterion of the TEP methodology has been applied based on the fact that increment in social welfare obtained by transmission reinforcement should be more than the investment cost of Transmission Company. The proposed methodology is well suited for nonlinear modeling of power systems as it reduces the complexity and computational burden of TEP problem substantially. To illustrate the efficacy of the proposed methodology, simulation result of Garver’s six-bus, IEEE 24-bus reliability test, and IEEE 662-bus systems have been presented.

Indian thermal power plant challenges and remedies via application of modified data envelopment analysis

AbstractThis paper evaluates the performance of coal‐fired thermal power plants in India for the year 2008–2009 using data envelopment analysis (DEA); subdividing the power plants into three categories depending on their scale—small, medium, and large. The classical DEA model is analyzed to identify the efficient ones from the whole gamut of plants run by various organizations of the central government, state government, and private sector. Slack analysis is carried out to explore the specific areas that need to be focused on, in quantitative terms, for the overall efficiency improvement. Further efficiency evaluation is extended from a single criterion‐based conventional approach to a multiple criteria oriented approach, and the resulting DEA models are more efficient and flexible in many aspects, particularly in discriminant and weight analysis. Results of multicriteria DEA (MCDEA) are substantiated with cross‐efficiency analysis by deploying the weights obtained by the MCDEA in the cross‐efficiency analysis. On the basis of the insights provided by the outcome of the analysis, both qualitative and quantitative measures are proposed for improvement of the plant performances. The result of this analysis may assist the management of the power plants to introspect and review their systems and processes for optimal use of resources. The methodology adopted in the present work can also be employed for deeper understanding of power plants in other parts of India as well as in other countries.

A multi criteria data envelopment analysis model to evaluate the efficiency of the Renewable Energy technologies

The growing concern in the negative effects of fossil fuels on the environment has forced a more intensive use of Renewable Energy sources which are considered an important solution to the large atmospheric pollution caused from fossil fuel combustion. In the electricity-generating industry, due to the high costs associated with the construction and operation of plants and the regulations that enforce productivity evaluations, efficiency is a key managerial concept. DEA is a non-parametric method which produces detailed information on the efficiency of a unit, to be measured without any assumptions regarding the functional form of the production function. In this paper, the efficiency of 13 Renewable Energy technologies is evaluated through a Multiple Criteria Data Envelopment Analysis (MCDEA) model. Two additional criterion, the minsum and minimax criterion, are included in the model which are the most restrictive than that defined in the classical DEA. The results show that the only Decision Making Unit rated as efficient is the Windpower 10 ≤ P ≤ 50 MW technology and it can be considered the only non-dominated solution.

The use of multi-criteria data envelopment analysis (MCDEA) for location–allocation problems in a fuzzy environment

This paper incorporates the concept of efficiency defined by data envelopment analysis (DEA) into location–allocation models in a fuzzy environment. It is shown how this incorporation can influence the pattern of facility location and the assignment of demands. Due to two major shortcomings, called low discrimination power and unrealistic weight distribution associated with classic form of DEA, a multi-criteria DEA is applied. Since the proposed model results in a fuzzy multi-objective non-linear programming, we present a solution procedure based on a modification of fuzzy parametric programming (FPP) and minimum deviation method.

Improving the discrimination power and weights dispersion in the data envelopment analysis

Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of peer decision making units (DMUs) with multiple input and outputs. Beside of its popularity, DEA has some drawbacks such as unrealistic input–output weights and lack of discrimination among efficient DMUs. In this study, two new models based on a multi-criteria data envelopment analysis (MCDEA) are developed to moderate the homogeneity of weights distribution by using goal programming (GP). These goal programming data envelopment analysis models, GPDEA-CCR and GPDEA-BCC, also improve the discrimination power of DEA.

Efficiency evaluation of a small number of DMUs: an approach based on Li and Reeves's model

This paper deals with the evaluation of Decision Making Units (DMU) when their number is not large enough to allow the use of classic Data Envelopment Analysis (DEA) models. To do so, we take advantage of the TRIMAP software when used to study the Li and Reeves MultiCriteria DEA (MCDEA) model. We introduce an evaluation measure obtained with the integration of one of the objective functions along the weight space. This measure allows the DMUs joint evaluation. This approach is exemplified with numerical data from some Brazilian electrical companies.

Measuring and Evaluating Efficiency of Hospitals Through Total Quality Management

ABSTRACT The purpose of this study is to measure and evaluate the efficiency of 12 hospitals in Turkey using a multi-criteria Data Envelopment Analysis (DEA) methodology. Number of beds, number of physicians and the critical factors of total quality management in the health care sector were used as inputs of the model. The outputs used in this analysis incorporated financial and non-financial performance of hospitals, number of outpatients and number of patient days. Performance of the hospitals was measured using subjective measures based on executive's perception of how their organization performed relative to the competition. Results provide management with information regarding the relatively best practice hospitals in the observation sets and locate the relatively inefficient hospitals by comparison with the best practice ones. At last some suggestions are made for the least efficient hospital.

Improving the discriminating power of DEA: focus on globally efficient units

We introduce in this paper the global efficiency approach as a means to improve the discriminating power of Data Envelopment Analysis (DEA). To discriminate further among the DEA efficient units, we deal only with the units that can maintain their efficiency score under common weighting structures. Then we proceed further to ranking the whole set of DEA efficient units. We compare the global efficiency approach with the multi-criteria DEA and the cross-efficiency approaches on the basis of characteristic numerical examples drawn from the literature.

Improving the discriminating power of DEA: focus on globally efficient units

We introduce in this paper the global efficiency approach as a means to improve the discriminating power of Data Envelopment Analysis (DEA). To discriminate further among the DEA efficient units, we deal only with the units that can maintain their efficiency score under common weighting structures. Then we proceed further to ranking the whole set of DEA efficient units. We compare the global efficiency approach with the multi-criteria DEA and the cross-efficiency approaches on the basis of characteristic numerical examples drawn from the literature.

A multi‐criteria data envelopment analysis model for measuring the productive efficiency of hospitals

This paper seeks to measure and evaluate the productive efficiency of health‐care organizations (hospitals) using a multicriteria Data Envelopment Analysis (DEA) methodology. DEA is a special linear programming model for deriving the comparative efficiency of multiple‐input multiple‐output Decision‐Making Units (DMUs). DEA provides management with information regarding the relatively best practice hospitals in the observation set and locates the relatively inefficient hospitals by comparison with the best practice ones. In addition, it indicates the magnitude of these inefficiencies. The paper selects a set of inputs and outputs for 15 hospitals for a three‐year period. The hospital input measures included are: number of bed days, number of physicians, and number of health personnel. The output measures included are: number of patient days, number of minor operations, and number of major operations. The DEA models are solved using an IBM PC optimization modeling system called LINDO (linear, interactive, discrete optimizer).