An Estimation of an Acceptable Efficiency Frontier Having an Optimum Resource Management Approach, with a Combination of the DEA-ANN-GA Technique (A Case Study of Branches of an Insurance Company)

Reza Sanei, Farhad Hosseinzadeh Lotfi (), Mohammad Fallah and Farzad Movahedi Sobhani
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Reza Sanei: Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1469669191, Iran
Farhad Hosseinzadeh Lotfi: Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran 1469669191, Iran
Mohammad Fallah: Department of Industrial Engineering, Central Tehran Branch, Islamic Azad University, Tehran 1469669191, Iran
Farzad Movahedi Sobhani: Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran 1469669191, Iran

Mathematics, 2022, vol. 10, issue 23, 1-21

Abstract: In this paper, a novel artificial intelligence technique for the estimation of near-optimal resource management is proposed. The model utilizes a two-stage data envelopment analysis to find the best-practice frontier of the decision-making units. By employing this data, a supervised multi-layer Artificial Neural Network is exercised. This network is capable of predicting the frontier for the near future by receiving input and mediator variables. In the next step, a genetic algorithm is formed to find an optimal input value for the artificial neural network, such that the overall performance of decision-making units in the near future is maximized. The proposed algorithm allows the managers to set some restrictions on the whole system, including the minimum efficiency and the maximum change on resources. The performance of the presented technique is reviewed on 31 branches of an insurance company, during the years 2015 to 2018. The results show that the developed algorithm can efficiently maximize the overall performance of decision-making units.

Keywords: benchmarking; efficiency frontier; data envelopment analysis; artificial neural networks; genetic algorithm; insurance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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