Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing
The wind energy industry is expanding in order to be able to meet the current and future energy demand, and is supported by governments in that renewable energy investment has been made. Optimal decision making (DM) in wind turbine manufacturing is required to guarantee the competitiveness of the bu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/9/1753 |
| _version_ | 1798024361269002240 |
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| author | Fausto Pedro García Márquez Isaac Segovia Ramírez Alberto Pliego Marugán |
| author_facet | Fausto Pedro García Márquez Isaac Segovia Ramírez Alberto Pliego Marugán |
| author_sort | Fausto Pedro García Márquez |
| collection | DOAJ |
| description | The wind energy industry is expanding in order to be able to meet the current and future energy demand, and is supported by governments in that renewable energy investment has been made. Optimal decision making (DM) in wind turbine manufacturing is required to guarantee the competitiveness of the business. This paper considers decision making for wind turbine manufacturing using a logical decision tree (LDT) and binary decision diagrams (BDD). A qualitative analysis of wind turbine manufacturing is carried out using logical decision trees. They are used for a qualitative study of the case study. Binary decision diagrams are used to obtain the Boolean function and, therefore, to carry out a quantitative analysis. Finally, an optimization of budgets is employed based on importance measures. There is no optimal method that can establish the importance measures. The following heuristic methods have been used to find a solution close to the optimal: Fussell-Vesely, Birnbaum and Criticality. The computational cost is reduced by ranking the events. The heuristic methods to establish the best rankings are: Top-Down-Left-Right, Level based method, AND based method, Breadth-First Search (BFS) and Depth First Search (DFS). A real case study is considered, in which a static and dynamic analysis is carried out. |
| first_indexed | 2024-04-11T18:02:18Z |
| format | Article |
| id | doaj.art-a914351ae4334be9a3437e6df96863a5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:02:18Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a914351ae4334be9a3437e6df96863a52022-12-22T04:10:27ZengMDPI AGEnergies1996-10732019-05-01129175310.3390/en12091753en12091753Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine ManufacturingFausto Pedro García Márquez0Isaac Segovia Ramírez1Alberto Pliego Marugán2Ingenium Research Group, Universidad de Castilla-La Mancha, 13071 Ciudad Real, SpainIngenium Research Group, Universidad de Castilla-La Mancha, 13071 Ciudad Real, SpainCUNEF-Ingenium, Colegio Universitario de Estudios Financieros, 28040 Madrid, SpainThe wind energy industry is expanding in order to be able to meet the current and future energy demand, and is supported by governments in that renewable energy investment has been made. Optimal decision making (DM) in wind turbine manufacturing is required to guarantee the competitiveness of the business. This paper considers decision making for wind turbine manufacturing using a logical decision tree (LDT) and binary decision diagrams (BDD). A qualitative analysis of wind turbine manufacturing is carried out using logical decision trees. They are used for a qualitative study of the case study. Binary decision diagrams are used to obtain the Boolean function and, therefore, to carry out a quantitative analysis. Finally, an optimization of budgets is employed based on importance measures. There is no optimal method that can establish the importance measures. The following heuristic methods have been used to find a solution close to the optimal: Fussell-Vesely, Birnbaum and Criticality. The computational cost is reduced by ranking the events. The heuristic methods to establish the best rankings are: Top-Down-Left-Right, Level based method, AND based method, Breadth-First Search (BFS) and Depth First Search (DFS). A real case study is considered, in which a static and dynamic analysis is carried out.https://www.mdpi.com/1996-1073/12/9/1753decision makinglogical decision treebinary decision diagramimportance measures |
| spellingShingle | Fausto Pedro García Márquez Isaac Segovia Ramírez Alberto Pliego Marugán Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing Energies decision making logical decision tree binary decision diagram importance measures |
| title | Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing |
| title_full | Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing |
| title_fullStr | Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing |
| title_full_unstemmed | Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing |
| title_short | Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing |
| title_sort | decision making using logical decision tree and binary decision diagrams a real case study of wind turbine manufacturing |
| topic | decision making logical decision tree binary decision diagram importance measures |
| url | https://www.mdpi.com/1996-1073/12/9/1753 |
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