دانلود رایگان مقاله انگلیسی طبقه بندی و پیش بینی متغیرهای پورت با استفاده از شبکه های بیزی - الزویر 2018

ABSTRACT

Many variables are included in planning and management of port terminals. They can be economic, social, environmental and institutional. Agent needs to know relationship between these variables to modify planning conditions. Use of Bayesian Networks allows for classifying, predicting and diagnosing these variables. Bayesian Networks allow for estimating subsequent probability of unknown variables, basing on know variables. In planning level, it means that it is not necessary to know all variables because their relationships are known. Agent can know interesting information about how port variables are connected. It can be interpreted as cause-effect relationship. Bayesian Networks can be used to make optimal decisions by introduction of possible actions and utility of their results. In proposed methodology, a data base has been generated with more than 40 port variables. They have been classified in economic, social, environmental and institutional variables, in the same way that smart port studies in Spanish Port System make. From this data base, a network has been generated using a non-cyclic conducted grafo which allows for knowing port variable relationships - parents-children relationships-. Obtained network exhibits that economic variables are – in cause-effect terms-cause of rest of variable typologies. Economic variables represent parent role in the most of cases. Moreover, when environmental variables are known, obtained network allows for estimating subsequent probability of social variables. It has been concluded that Bayesian Networks allow for modeling uncertainty in a probabilistic way, even when number of variables is high as occurs in planning and management of port terminals.

3. Conclusions

The most decision-making category, as network obtained by using the algorithm K2 shows, is institutional category, then economic and social at the same height, and finally environmental category. Management systems supporting decision-making includes quality management systems, scorecards, market characterization campaigns, etc, and they are represented by herramgestion_dimins. It is considered as a parent variable in network, so arrows only start on it. The same goes for transconcu_dimins which represents initiatives to ensure that any operator could provide services in port or qualify for a concession because operator can know, in a transparent way, conditions to operate and administrative mechanisms governing this process. It is a father node in network. Furthermore, transcocu_dimins is decision-making variable, so it appears in network as a “node” variable and bows only start on it. So, a divergent connection is created and this father node throws its bows toward several of its sons, that is to say, arrows start on it and go to its sons.

Other essential variable in network structure is mercservidos_dimins. 10 arrows star on it and go to 10 different nodes. These are effects of structure and main good traffic evolution, so they are social, economic, institutional and environmental effects. That is to say, served markets have effects on rates, delivery framework and regulation of port services the number of companies operating in the port (institutional category). It has effects on EBITDA, EBITDA/tonne, public investment relative to cash flow and: income from employment and activity rates among others too (economic category). Even, about social status, it has effects on variables representing port community employment, job security and training services and health work, among others. Finally, in environmental category, served markets causes different grades of environmental management systems implementation (EMAS, ISO 14001 y PERLS), economic resource investment and investments associated to implementation, certification and maintenance of environmental management system. Therefore, served markets are a very important variable in planning from a sustainable perspective