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Optimal decision-making via binary decision diagrams for investments under a risky environment

Author

Listed:
  • Alberto Pliego Marugán
  • Fausto Pedro García Márquez
  • Benjamin Lev

Abstract

This paper presents two methods for supporting investments and resource allocation in a constrained risky environment. These methods are based on the application of logical decision trees and binary decision diagrams as an approach that allows quantitative analysis of a qualitative study. The scenario considered in this paper is a decision-making process under risk environment, where stochastic variables are considered. The two novel procedures are introduced to facilitate the resource allocation as the objective of the decision-making process. The first procedure uses the analytic expression provided by binary decision diagrams as an objective function of a non-linear programing model. The second procedure introduces an importance measure that takes into account some external constraints, unlike the classical importance measures that only consider the topology of the tree. The first technique will optimise the outcomes and the second will provide a good approximation of the outcomes using simpler calculations.

Suggested Citation

  • Alberto Pliego Marugán & Fausto Pedro García Márquez & Benjamin Lev, 2017. "Optimal decision-making via binary decision diagrams for investments under a risky environment," International Journal of Production Research, Taylor & Francis Journals, vol. 55(18), pages 5271-5286, September.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:18:p:5271-5286
    DOI: 10.1080/00207543.2017.1308570
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    Citations

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    Cited by:

    1. Kisvari, Adam & Lin, Zi & Liu, Xiaolei, 2021. "Wind power forecasting – A data-driven method along with gated recurrent neural network," Renewable Energy, Elsevier, vol. 163(C), pages 1895-1909.
    2. Segovia Ramírez, Isaac & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2022. "A novel approach to optimize the positioning and measurement parameters in photovoltaic aerial inspections," Renewable Energy, Elsevier, vol. 187(C), pages 371-389.
    3. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    4. Yan-Feng Li & Hong-Zhong Huang & Jinhua Mi & Weiwen Peng & Xiaomeng Han, 2022. "Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability," Annals of Operations Research, Springer, vol. 311(1), pages 195-209, April.
    5. Huerta Herraiz, Álvaro & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Photovoltaic plant condition monitoring using thermal images analysis by convolutional neural network-based structure," Renewable Energy, Elsevier, vol. 153(C), pages 334-348.
    6. García Márquez, Fausto Pedro & Peco Chacón, Ana María, 2020. "A review of non-destructive testing on wind turbines blades," Renewable Energy, Elsevier, vol. 161(C), pages 998-1010.
    7. Pliego Marugán, Alberto & Peco Chacón, Ana María & García Márquez, Fausto Pedro, 2019. "Reliability analysis of detecting false alarms that employ neural networks: A real case study on wind turbines," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    8. Arcos Jiménez, Alfredo & Gómez Muñoz, Carlos Quiterio & García Márquez, Fausto Pedro, 2019. "Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 2-12.
    9. Mi, Jinhua & Beer, Michael & Li, Yan-Feng & Broggi, Matteo & Cheng, Yuhua, 2020. "Reliability and importance analysis of uncertain system with common cause failures based on survival signature," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    10. Jinhua Mi & Yuhua Cheng & Yufei Song & Libing Bai & Kai Chen, 2022. "Application of dynamic evidential networks in reliability analysis of complex systems with epistemic uncertainty and multiple life distributions," Annals of Operations Research, Springer, vol. 311(1), pages 311-333, April.
    11. Jiménez, Alfredo Arcos & García Márquez, Fausto Pedro & Moraleda, Victoria Borja & Gómez Muñoz, Carlos Quiterio, 2019. "Linear and nonlinear features and machine learning for wind turbine blade ice detection and diagnosis," Renewable Energy, Elsevier, vol. 132(C), pages 1034-1048.
    12. Mahantesh Marikatti & N. R. Banapurmath & V. S. Yaliwal & Y.H. Basavarajappa & Manzoore Elahi M Soudagar & Fausto Pedro García Márquez & MA Mujtaba & H. Fayaz & Bharat Naik & T.M. Yunus Khan & Asif Af, 2020. "Hydrogen Injection in a Dual Fuel Engine Fueled with Low-Pressure Injection of Methyl Ester of Thevetia Peruviana (METP) for Diesel Engine Maintenance Application," Energies, MDPI, vol. 13(21), pages 1-27, October.
    13. Peinado Gonzalo, Alfredo & Pliego Marugán, Alberto & García Márquez, Fausto Pedro, 2020. "Survey of maintenance management for photovoltaic power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).

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