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Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing

Author

Listed:
  • Fausto Pedro García Márquez

    (Ingenium Research Group, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Isaac Segovia Ramírez

    (Ingenium Research Group, Universidad de Castilla-La Mancha, 13071 Ciudad Real, Spain)

  • Alberto Pliego Marugán

    (CUNEF-Ingenium, Colegio Universitario de Estudios Financieros, 28040 Madrid, Spain)

Abstract

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.

Suggested Citation

  • Fausto Pedro García Márquez & Isaac Segovia Ramírez & Alberto Pliego Marugán, 2019. "Decision Making using Logical Decision Tree and Binary Decision Diagrams: A Real Case Study of Wind Turbine Manufacturing," Energies, MDPI, vol. 12(9), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1753-:d:229537
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    References listed on IDEAS

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    1. Dowon Han & Young Gun Heo & Nak Joon Choi & Sang Hyun Nam & Kyoung Ho Choi & Kyung Chun Kim, 2018. "Design, Fabrication, and Performance Test of a 100-W Helical-Blade Vertical-Axis Wind Turbine at Low Tip-Speed Ratio," Energies, MDPI, vol. 11(6), pages 1-17, June.
    2. Lopez, David A & Van Slyke, William J, 1977. "Logic tree analysis for decision making," Omega, Elsevier, vol. 5(5), pages 614-617.
    3. Cascetta, Ennio & Cartenì, Armando & Pagliara, Francesca & Montanino, Marcello, 2015. "A new look at planning and designing transportation systems: A decision-making model based on cognitive rationality, stakeholder engagement and quantitative methods," Transport Policy, Elsevier, vol. 38(C), pages 27-39.
    4. Fausto Pedro García Márquez & Alberto Pliego Marugán & Jesús María Pinar Pérez & Stuart Hillmansen & Mayorkinos Papaelias, 2017. "Optimal Dynamic Analysis of Electrical/Electronic Components in Wind Turbines," Energies, MDPI, vol. 10(8), pages 1-19, July.
    5. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    6. Vijaya Dixit & Rajiv Srivastava K. & Atanu Chaudhuri, 2015. "Project network-oriented materials management policy for complex projects: a Fuzzy Set Theoretic approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(10), pages 2904-2920, May.
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    Cited by:

    1. Chee Sun Lee & Peck Yeng Sharon Cheang & Massoud Moslehpour, 2022. "Predictive Analytics in Business Analytics: Decision Tree," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 1-30, March.

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