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

Beatriz Molina Serrano, Nicoleta González-Cancelas, Francisco Soler-Flores, Samir Awad-Nuñez and Alberto Camarero Orive
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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

Logistics, 2018, vol. 2, issue 1, 1-16

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.

Keywords: Bayesian Networks; graph theory; sustainability; port management; artificial networks (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2018
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