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Use of Bayesian Networks to Analyze Port Variables in Order to Make Sustainable Planning and Management Decision

Author

Listed:
  • Beatriz Molina Serrano

    (Departamento de Ingeniería Civil, Transportes, Universidad Politécnica de Madrid, 28040 Madrid, Spain)

  • Nicoleta González-Cancelas

    (Departamento de Ingeniería Civil, Transportes, Universidad Politécnica de Madrid, 28040 Madrid, Spain)

  • Francisco Soler-Flores

    (Departamento Tessella-Altran World Class Center for Analytics, Altran Innovación, 28022 Madrid, Spain)

  • Samir Awad-Nuñez

    (Departamento de Ingeniería Civil, Universidad Europea de Madrid, Madrid 28670, Spain)

  • Alberto Camarero Orive

    (Departamento de Ingeniería Civil, Transportes, Universidad Politécnica de Madrid, 28040 Madrid, Spain)

Abstract

In the current economic, social and political environment, society demands a greater variety of outcomes from the public logistics sector, such as efficiency, efficiency of managed resources, greater transparency and business performance. All of them are an indispensable counterpart for its recognition and support. In case of port planning and management, many variables are included. Use of Bayesian Networks allows to classify, predict and diagnose these variables and even to estimate the subsequent probability of unknown variables, basing on the known ones. Research includes a data base with more than 40 variables, which have been classified as smart port studies in Spain. Then a network was generated using a non-cyclic conducted grafo, which shows port variable relationships. As conclusion, economic variables are cause of the rest of categories and they represent a parent role in the most of cases. Furthermore, if environmental variables are known, subsequent probability of social variables can be estimated.

Suggested Citation

  • Beatriz Molina Serrano & Nicoleta González-Cancelas & Francisco Soler-Flores & Samir Awad-Nuñez & Alberto Camarero Orive, 2018. "Use of Bayesian Networks to Analyze Port Variables in Order to Make Sustainable Planning and Management Decision," Logistics, MDPI, vol. 2(1), pages 1-16, January.
  • Handle: RePEc:gam:jlogis:v:2:y:2018:i:1:p:5-:d:126418
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    References listed on IDEAS

    as
    1. Nguyen Thi Thuy Hong & Hoang Thi Lich & Bui Thi Thanh Nga, 2017. "The Social Investment Capital and the Cargo Volume Transported by Sea: A VAR Approach for Vietnam," Logistics, MDPI, vol. 1(2), pages 1-9, September.
    2. Y. H. Venus Lun & Kee-hung Lai & Christina W. Y. Wong & T. C. E. Cheng, 2014. "Green shipping practices and firm performance," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(2), pages 134-148, March.
    3. Trucco, P. & Cagno, E. & Ruggeri, F. & Grande, O., 2008. "A Bayesian Belief Network modelling of organisational factors in risk analysis: A case study in maritime transportation," Reliability Engineering and System Safety, Elsevier, vol. 93(6), pages 845-856.
    4. Castillo, Enrique & Menéndez, José María & Sánchez-Cambronero, Santos, 2008. "Predicting traffic flow using Bayesian networks," Transportation Research Part B: Methodological, Elsevier, vol. 42(5), pages 482-509, June.
    5. Taih-Cherng Lirn & Hsiao-Wen Lin & Kuo-Chung Shang, 2014. "Green shipping management capability and firm performance in the container shipping industry," Maritime Policy & Management, Taylor & Francis Journals, vol. 41(2), pages 159-175, March.
    6. Janssens, Davy & Wets, Geert & Brijs, Tom & Vanhoof, Koen & Arentze, Theo & Timmermans, Harry, 2006. "Integrating Bayesian networks and decision trees in a sequential rule-based transportation model," European Journal of Operational Research, Elsevier, vol. 175(1), pages 16-34, November.
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