Using Genetic Algorithms for Maximizing Technical Efficiency in Data Envelopment Analysis

González, Martín; López-Espín, José J.; Aparicio, Juan; Giménez, Domingo; Pastor, Jesús T.

doi:10.1016/j.procs.2015.05.257

Cited by 15 publications

(9 citation statements)

References 13 publications

(12 reference statements)

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“…Before proposing the model based on DDEA and OCT, it is important to know how to solve these kinds of models. According to Gonzalez et al (2015), some metaheuristics leads to unsatisfactory solutions when solving DEA problems, and Alves Junior and Cari (2017) state that solving a nonlinear OCT problems without a good initial guess of the solution or even with some metaheuristics, as genetic algorithm, takes an unworkable time to be solved.…”

Section: Proposed Modelmentioning

confidence: 99%

Insider understanding of country development: a novel application of Optimal Control Theory and Data Envelopment Analysis for benchmarking performance of Chilean and Brazilian companies

Alves

Yushimito

Gude

et al. 2019

CEREM

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Aim: If companies manage their inventory inefficiently, inventory costs can increase significantly due to shortages, overstocking, and risks. Inventory management is critical for company’s success which, in turn, impacts on countries’ development. This paper aims to investigate the efficiency of inventory control systems of companies from Brazil and Chile through Optimal Control Theory and Data Envelopment Analysis.Design/Research methods: Data was collected from Chilean and Brazilian companies covering different industries in which both countries are mostly dependent A new approach using OCT and DEA is applied for dealing with inventory, production, and demand in Dynamic DEA model to benchmark companies’ production-inventory systems.Conclusions/findings: The results show efficient companies among evaluated industries. Such companies are related mainly to Brazilian commerce and Chilean exports. Based on findings, it was possible to identify patterns and relationship among companies and its inventory management.Originality/value of the article: This paper fills a gap in studies including demand, production, and inventory in Dynamic DEA by using OCT to forewarn unrealistic results and observing companies’ behavior. Besides that, this approach is particularly useful for developing countries in this context, determining benchmarks for the most inefficient firms in each sector.Implications of the research: The results show (1) which companies should focus more on improving inventory management, (2) which companies should be used as benchmarks, and (3) it highlights the reasons of different performance of companies in each country.Limitations of the research: For future research, it is suggested including variables and analysis of social and environmental impacts.

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Section: Proposed Modelmentioning

confidence: 99%

Insider understanding of country development: a novel application of Optimal Control Theory and Data Envelopment Analysis for benchmarking performance of Chilean and Brazilian companies

Alves

Yushimito

Gude

et al. 2019

CEREM

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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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“…The application of the Principle of Least Action has been recently studied from a metaheuristic perspective (Benavente et al [8], López-Espín et al [9] and González et al [10]). In [8,9] heuristics were used to generate valid solutions for a subset of restrictions of the problem, while in [10] all the constraints are incorporated, the heuristics are improved, and new ones are developed, thereby generating initial populations of solutions that satisfy all constraints.…”

Section: Introductionmentioning

confidence: 99%

“…Our paper takes up where González et al [10] left off in the application of metaheuristics to the approach in [1]. The improvement of previous heuristics for the generation of valid solutions is a possible option, but greatly limits the search for valid solutions for large problem sizes, because when the number of variables grows, the number of valid solutions decreases.…”

Section: Introductionmentioning

confidence: 99%

A Parallel Application of Matheuristics in Data Envelopment Analysis

González

López-Espín

Aparicio

et al. 2018

Distributed Computing and Artificial Intelligence, 15th International Conference

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Data Envelopment Analysis (DEA) is a non-parametric methodology for estimating technical efficiency and benchmarking. In general, it is desirable that DEA generates the efficient closest targets as benchmarks for each assessed unit. This may be achieved through the application of the Principle of Least Action. However, the mathematical models associated with this principle are based fundamentally on combinatorial NP-hard problems, difficult to be solved. For this reason, this paper uses a parallel matheuristic algorithm, where metaheuristics and exact methods work together to find optimal solutions. Several parallel schemes are used in the algorithm, being possible for them to be configured at different stages of the algorithm. The main intention is to divide the number of problems to be evaluated in equal groups, so that they are resolved in different threads. The DEA problems to be evaluated in this paper are independent of each other, an indispensable requirement for this algorithm. In addition, taking into account that the main algorithm uses exact methods to solve the mathematical problems, different optimization software has been evaluated to compare their performance when executed in parallel. The method is competitive with exact methods, obtaining fitness close to the optimum with low computational time.

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Using Genetic Algorithms for Maximizing Technical Efficiency in Data Envelopment Analysis

Cited by 15 publications

References 13 publications

Insider understanding of country development: a novel application of Optimal Control Theory and Data Envelopment Analysis for benchmarking performance of Chilean and Brazilian companies

Insider understanding of country development: a novel application of Optimal Control Theory and Data Envelopment Analysis for benchmarking performance of Chilean and Brazilian companies

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

A Parallel Application of Matheuristics in Data Envelopment Analysis

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