Modelagem da decisão por pacotes de viagem: um estudo usando processo de hierarquia analítica AHP

The objective of this dissertation is to propose a Multi Criteria Decision Aid Model to be used by the costumers of the travel agencies and help them to choose the best package travel. The main objective is to contribute for the simplification of the travel package decision choice from the identification of the models of values and preference of the customers and applying them to the existing package. It is used the Analytic Hierarchy Process (AHP) method to structuralize a decision hierarchic model composed by six criteria (package cost, hotel category, security of the city, travel time, direct flight and position in ranking of the 10 most visited destination) and five real alternatives of packages for a holiday of three days created from travel agency data. The decision analysis was realized for the choice of a travel package by a group composed by two couples that regularly travels together, to which was asked to do a pairwise judgment of the criteria and the alternatives. The mains results show that, although been a group that travels together, there are different models of values in the weights of the criteria and a certain convergence in the scales of preferences of the alternatives in the criteria. It was not pointed a dominant alternative for all the members of the group separately, but an analysis of a total utility of the group shows a classification and an order of the travel packages and an alternative clearly in front of the others. The sensitivity analysis revels that there are changes in the ranking, but the two alternatives best classified in the normal analysis are the same ones in the sensitivity analysis, although with the positions changed. The analysis also led to a simplification of the process with the exclusion of alternatives dominated for the others ones. As main conclusion, it is evaluated that the model and method suggested allow a simplification of the decision process in the choice of travel packages

Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity