Benchmarking in Data Envelopment Analysis: An Approach Based on Genetic Algorithms and Parallel Programming

Aparicio, Juan; López-Espín, José J.; Martinez-Moreno, Raul; Pastor, Jesús T.

doi:10.1155/2014/431749

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…Feroz et al (2003) proposed a DEA solution for financial statement analysis. Aparicio et al (2013) did benchmarking in data envelopment analysis based on genetic algorithms. Here, this method is applied to find the weight for financial statements of corporations applied in part 3.1, where related weights are (0, 0.35, 0.22, 0.05, 0.05, 0.22, 0.2) Dual analysis is a common approach to the optimization problem.…”

Section: Source: Researcher Resultsmentioning

confidence: 99%

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Application of Predictive Methods to Financial Data Sets

Habibi

2021

Financial Internet Quarterly

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Financial data sets are growing too fast and need to be analyzed. Data science has many different techniques to store and summarize, mining, running simulations and finally analyzing them. Among data science methods, predictive methods play a critical role in analyzing financial data sets. In the current paper, applications of 22 methods classified in four categories namely data mining and machine learning, numerical analysis, operation research techniques and meta-heuristic techniques, in financial data sets are studied. To this end, first, literature reviews on these methods are given. For each method, a data analysis case (as an illustrative example) is presented and the problem is analyzed with the mentioned method. An actual case is given to apply those methods to solve the problem and to choose a better one. Finally, a conclusion section is proposed.

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Section: Source: Researcher Resultsmentioning

confidence: 99%

“…Hillier and Lieberman (2012) described the financial applications of Kuhn-Tucker equations. Aparicio et al (2013) used the DEA technique for benchmarking based on genetic algorithms and parallel programming. Cai et al (2014) used -dual interior-point methods for linear optimization.…”

mentioning

confidence: 99%

Application of Predictive Methods to Financial Data Sets

Habibi

2021

Financial Internet Quarterly

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“…Unlike the CCR model which is shown in Expression (2), the BCC model shown in Equation 3allows for variable returns to scale, but both models are based on radial projections to the production frontier. However, many other approaches give freedom to the projection so that the final efficient targets do not conserve the mix of inputs and outputs [20].…”

Section: Data Envelopment Analysismentioning

confidence: 99%

Efficiency of Moroccan Seaports: Application of DEA Using Window Analysis

Birafane¹,

Abdi²

2019

ENG

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The efficiency of 8 seaports in the Kingdom of Morocco region was evaluated. Two associated studies were conducted based on data collected for 4 years (2014-2017), Standard Data Envelopment Analysis (DEA) method was used in the first analysis through CCR and BCC models, and thus a scale of efficiency analysis was used in a second analysis. By using both methods, enhanced insight into the efficiency situation is collected, proving subsequently that a majority of the container ports and terminals in the Kingdom of Morocco are found to be technically inefficient. The research concludes through returns to scale method how port resources should be utilized in the aim to impulse inefficient ports perform better.

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“…In view of the preceding discussion, from a computational point of view, the determination of the least distance in DEA has not yet been satisfactorily solved, and consequently, the effort to apply new methods to overcome the problem is, therefore, justified. In this respect, other related papers are those by Martinez-Moreno et al (2013), Lopez-Espin et al (2014), Aparicio et al (2014b) and Gonzalez et al (2015), who apply genetic algorithms, meta-heuristics and parallel programming for determining closest efficient targets in DEA.…”

Section: Modeling and Computational Aspectsmentioning

confidence: 99%

A survey on measuring efficiency through the determination of the least distance in data envelopment analysis

Aparicio

2016

JCC

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Purpose -The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated. Design/methodology/approach -DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a "benchmarking" target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the "furthest" efficient projection to the evaluated DMU. The projected point

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Benchmarking in Data Envelopment Analysis: An Approach Based on Genetic Algorithms and Parallel Programming

Cited by 12 publications

References 32 publications

Application of Predictive Methods to Financial Data Sets

Application of Predictive Methods to Financial Data Sets

Efficiency of Moroccan Seaports: Application of DEA Using Window Analysis

A survey on measuring efficiency through the determination of the least distance in data envelopment analysis

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