The healthcare environment presents a large volume of personal and sensitive patient data that needs to be available and secure. Information and communication technology brings a new reality to healthcare, promoting improvements, agility and integration. Regarding high-level and complex decision-making scenarios, the Brazilian Navy (BN), concerning its healthcare field, is seeking to provide better management of its respective processes in its hospital facilities, allowing accurate control of preventive and curative medicine to members who work or have served there in past years. The study addresses the understanding, structure and clarifying variables related to the feasibility of technological updating and installing of a Hospital Information System (HIS) for BN. In this scenario, through interviews and analysis of military organization business processes, criteria and alternatives were established based on multi-criteria methodology as a decision aid. As methodological support for research and data processing, THOR 2 and PROMETHEE-SAPEVO-M1 methods were approached, both based on the scenarios of outranking alternatives based on the preferences established by the stakeholders in the problem. As a result of the methodological implementation, we compare the two implemented methods in this context, exposing the Commercial Software Purchase and Adoption of Free Software, integrated into Customization by the Marine Studies Foundation, as favorable actions to be adopted concerning HIS feasibility. This finding generates a comprehensive discussion regarding the BN perspective and changes in internal development in the military environment, prospecting alignment to the culture of private organizations in Information Technology for healthcare management. In the end, we present some conclusions concerning the study, exploring the main points of the decision-making analysis and for future research.
This paper approaches the problem of ballast water treatment in ships. This has been identified as one of the four greatest threats to the world’s oceans. Solutions that have been considered for solving the problem are alternative water treatment technologies. In the case study reported in this paper three major water treatment technologies have been evaluated with the help of twenty-six criteria, quantitative as well as qualitative by using two discrete multicriteria methods, TODIM and THOR 2. The THOR 2 consists of the axiomatic evolution of the THOR method and both THOR 2 and THOR are made available through the THOR Web platform. Five groups of evaluation criteria are then considered: practicality; biological effectiveness; cost/benefit ratio; time frame for the implementation of standards; and environmental impact of the process' sub-products. In this paper a case study on choosing a ballast water treatment technology is presented. Three alternative ballast water management technologies are proposed by experts in the field and are evaluated with the help of twenty-six criteria, quantitative as well as qualitative. Each ballast water management method is described by a list of twenty-six attributes or criteria. After setting the problem in a clear way and consulting different experts, the two separate applications of both TODIM and THOR 2 are performed. What is denoted as Management Method #1 is indeed chosen as the best alternative according to both methods. The conclusion is that those two methods, although conceptually and analytically quite different, lead essentially to the same main results. Two other applications of both TODIM and THOR have indeed confirmed the convergence of results in spite of the conceptual and technical differences between the two methods. This suggests that formulating a decision problem in a correct, clear-cut way can be at least as important as the technical characteristics of the method per se.
The present study aims to propose an axiomatic evolution of the method, called THOR 2, based on the analysis of the original algorithm. It was proposed, in the evolution, the distinction in the attribution of weights in the sum of scores as well as the multiplication of the value of the criterion weight by the fuzzy-rough index in all preference relations. This functionality allows that, in the absence of data to fill in the classification of alternatives and weights in the decision matrix, it is possible to estimate the data and assign a low pertinence value for attributing that data, thus avoiding the elimination of the alternative or criterion due to the absence of the data. In order to validate the pertinence function proposed for THOR 2, an analysis of the ranking of alternatives in three different scenarios was carried out. In this way, the scenarios were simulated in which there was an absence of data in the original decision matrix. The analysis aimed to compare the result of the ranking of the alternatives when there is no data with the situation that the decision matrix is complete (all data are available), observing the impact on the ranking of the alternatives. In all scenarios that used data estimated in conjunction with the pertinence function, the ranking was kept in line with the ranking in the initial situation. However, when it was decided to exclude the criteria, the ordering was different from the ordering in the situation of origin.INDEX TERMS Brazilian Navy, Decision Support Systems, Multiple Criteria Decision Analysis, THOR 2 I. INTRODUCTIONResearch to deal with inaccurate information in multicriteria decision analysis has been ongoing over the past few decades [1]. According to Xiao [2], there are a variety of methods to deal with uncertainty, being applied in several areas such as selection, workflow scheduling, prediction, failure mode analysis and effects analysis (FMEA), fusion, evidence reasoning, medical diagnosis, decision making and classification. In many of the approaches, data/preferences on the values of attributes and /or compensation weights are required to be the most accurate or representative of the decision makers' preferences. However, providing such accurate data is not always an easy task for decision makers since there may be the inclusion of unattainable attributes to reflect social and environmental impacts [3]. Since not all data are always available, to apply a multi-criteria decision analysis (MCDA), it is essential to eliminate variants without data or complete the data [4]. The exclusion of criteria or alternatives, however, often causes important data to be disregarded, impacting the quality of the ordering generated by the decision algorithms, as occurs in traditional multi-criteria decision support methods such as Analytic Hierarchy Process (AHP) [5], ÉLimination et Choix Traduisant la REalité (ELECTRE) [6] and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) [7], for example. The following research problem arises: "If you do not ...
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