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.
Highlights
Sustainable development is being applied emergently by transport authorities as it has been made in other activity sectors and industries all over the World
In more recent studies [31], Bayesian Networks are used to find the logistic potential of a country, and, in others [32], main variables are defined and virtual scenarios inferences are determined in order to analyse container terminals scenarios using probabilistic graphical models
It is possible to create ones, because Bayesian Networks usually use this kind of variables
Summary
The objective to provide a toolanalysis to managers planners through sustainability variables the international literature on port exploitation and planning. Most used establishes the reference criteria which terminals should be exploited It determines what bibliography deals with planning and management, but it does not include sustainability. This kind of a terminal moves away from or approaches the reference farm. In more recent studies [31], Bayesian Networks are used to find the logistic potential of a country, and, in others [32], main variables are defined and virtual scenarios inferences are determined in order to analyse container terminals scenarios using probabilistic graphical models. Able to establish a model of planning zones of logistical activities by using Bayesian Networks [35]
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