Optimization for Risk Decision-Making Through Simulated Annealing
In this paper, a computational methodology combining the simulated annealing algorithm with two machine learning techniques to select a near-optimal safeguard set for business risk response is presented. First, a mathematical model with four types of risk factor responses (avoid, mitigate, transfer,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9125878/ |
| _version_ | 1818578296874467328 |
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| author | Marta Lilia Erana-Diaz Marco Antonio Cruz-Chavez Rafael Rivera-Lopez Beatriz Martinez-Bahena Erika Yesenia Avila-Melgar Martin Heriberto Cruz-Rosales |
| author_facet | Marta Lilia Erana-Diaz Marco Antonio Cruz-Chavez Rafael Rivera-Lopez Beatriz Martinez-Bahena Erika Yesenia Avila-Melgar Martin Heriberto Cruz-Rosales |
| author_sort | Marta Lilia Erana-Diaz |
| collection | DOAJ |
| description | In this paper, a computational methodology combining the simulated annealing algorithm with two machine learning techniques to select a near-optimal safeguard set for business risk response is presented. First, a mathematical model with four types of risk factor responses (avoid, mitigate, transfer, and accept) is constructed. Then, the simulated annealing algorithm is applied to find a set of near-optimal solutions to the model. Next, these solutions are processed by the k-means clustering algorithm for identifying three categories, and with a decision tree classifier, the most relevant elements of each one are obtained. Finally, the categorized solutions are shown to the decision-makers through a user interface. These stages are designed with the aim of the users can take an appropriate safeguard set and develop one specific and optimal program to respond to business risk factors. The results generated by the proposed approach are reached in a reasonable time using less computational resources than those used by other procedures. Furthermore, the best results obtained by the simulated annealing algorithm use a lower business budget, and they have a relative-error less than 0.0013% of the optimal solution given by a deterministic method. |
| first_indexed | 2024-12-16T06:43:34Z |
| format | Article |
| id | doaj.art-24ba6df4c2484129a496f4a5ebc76c16 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T06:43:34Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-24ba6df4c2484129a496f4a5ebc76c162022-12-21T22:40:38ZengIEEEIEEE Access2169-35362020-01-01811706311707910.1109/ACCESS.2020.30050849125878Optimization for Risk Decision-Making Through Simulated AnnealingMarta Lilia Erana-Diaz0Marco Antonio Cruz-Chavez1https://orcid.org/0000-0001-9967-3886Rafael Rivera-Lopez2https://orcid.org/0000-0002-5254-4195Beatriz Martinez-Bahena3Erika Yesenia Avila-Melgar4Martin Heriberto Cruz-Rosales5Research Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca, MexicoResearch Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca, MexicoComputation and Systems Department, National Technological Institute of Mexico/Veracruz Technological Institute, Veracruz, MexicoResearch Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca, MexicoResearch Center in Engineering and Applied Sciences, Autonomous University of Morelos State (UAEM), Cuernavaca, MexicoFaculty of Accounting, Administration Informatics, Autonomous University of Morelos State (UAEM), Cuernavaca, MexicoIn this paper, a computational methodology combining the simulated annealing algorithm with two machine learning techniques to select a near-optimal safeguard set for business risk response is presented. First, a mathematical model with four types of risk factor responses (avoid, mitigate, transfer, and accept) is constructed. Then, the simulated annealing algorithm is applied to find a set of near-optimal solutions to the model. Next, these solutions are processed by the k-means clustering algorithm for identifying three categories, and with a decision tree classifier, the most relevant elements of each one are obtained. Finally, the categorized solutions are shown to the decision-makers through a user interface. These stages are designed with the aim of the users can take an appropriate safeguard set and develop one specific and optimal program to respond to business risk factors. The results generated by the proposed approach are reached in a reasonable time using less computational resources than those used by other procedures. Furthermore, the best results obtained by the simulated annealing algorithm use a lower business budget, and they have a relative-error less than 0.0013% of the optimal solution given by a deterministic method.https://ieeexplore.ieee.org/document/9125878/Risk factor to bankruptcymetaheuristicmachine learningk-meansdecision trees |
| spellingShingle | Marta Lilia Erana-Diaz Marco Antonio Cruz-Chavez Rafael Rivera-Lopez Beatriz Martinez-Bahena Erika Yesenia Avila-Melgar Martin Heriberto Cruz-Rosales Optimization for Risk Decision-Making Through Simulated Annealing IEEE Access Risk factor to bankruptcy metaheuristic machine learning k-means decision trees |
| title | Optimization for Risk Decision-Making Through Simulated Annealing |
| title_full | Optimization for Risk Decision-Making Through Simulated Annealing |
| title_fullStr | Optimization for Risk Decision-Making Through Simulated Annealing |
| title_full_unstemmed | Optimization for Risk Decision-Making Through Simulated Annealing |
| title_short | Optimization for Risk Decision-Making Through Simulated Annealing |
| title_sort | optimization for risk decision making through simulated annealing |
| topic | Risk factor to bankruptcy metaheuristic machine learning k-means decision trees |
| url | https://ieeexplore.ieee.org/document/9125878/ |
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