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

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.

Business process management systems in port processes: a systematic literature review

“…In this context, there are many initiatives around the transformation of ports into intelligent ports and the automation of logistics at European level. These new trends seek to respond to the challenges of optimisation of port space, port operation times, transport costs and productivity levels, maximization of loading and unloading flows challenges, physical limitations in the process, consumption of natural resources and reduction of environmental impact (Molina Serrano et al, 2018). Industry 4.0 is undoubtedly the new paradigm that is permeating the technological management of organizations and the management of ports.…”