Using MDS to compute the contribution of the experts in a Delphi forecast associated to a naval operation’s DSS - Teodoro, M. F.; Simões-Marques, M.. J.; Nunes, I.; Calhamonas, G.; Andrade, M. A. P. (20th International Conference on Computational Science and Its Applications, ICCSA 2020, Conference proceedings, 2020)
The Portuguese Navy gave financial support to THEMIS project under the aim of the development of a decision support system to get optimal decisions in short time in a disaster context, optimizing the decision chain, allowing to get a better performance of tasks execution allowing a reduction of costs. In [14, 17], the authors have considered the facilities and high qualified staff of Portuguese Navy and proposed a variant of the Delphi method, a method that is exceptionally useful where the judgments of individuals are considered as an important information source. They proposed a system that prioritize certain teams for specific incidents taking into account the importance of each team that acts in case of emergency. In the present work we propose a distinct method of computing the weights that represent the importance given to experts opinion in the Delphi method used in [14, 17] under the idea that shall not depend on the years of experience of each expert exclusively but also shall be considered the kind of expert experience. To justify this suggestion we have used hierarchical classification, allowing to identify different padrons for experts with the “same experience”. Also discriminant analysis and multidimensional scaling revealed to be adequate techniques for this issue. We can classify the experience of each expert evaluating the similarity/distance between the individuals in the group of proposed experts and compare with the number of consensus presented. In this manuscript we propose an alternative way of weighting the experts experience that contributes to a decision support system capable to prioritize a set of teams for certain disaster incidents involving maritime issues. The decision support system is still been tested but, with this work, we hope to have given an improvement to its optimization.