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New Refinement of an Intelligent System Design for Naval Operations

Conference paper
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Part of theLecture Notes in Mechanical Engineeringbook series (LNME)

Abstract

To better optimize the decision chain in a maritime disaster context, allowing a reduction both in time of support and costs, recently, the authors considered the facilities and high qualified staff of Portuguese Navy proposing a variant of the Delphi method, a method where the opinion of each element of a team is considered an important information source to contribute to a decision, implementing a system that prioritize certain teams for perform specific in incidents, taking into account the importance of each team that acts in case of a disaster occurrence.

Continuing the work developed recently and extending the technique used before, in the present manuscript, we propose an adjustment of each expert opinion weight when the Delphi method is applied. The weight of each expert does not not depend on years of expert experience exclusively but also from the type of such experience. Firstly, we have used the hierarchical classification, allowing to identify different patterns for experts with the “‘same experience”’. Also discriminant analysis and multidimensional scaling revealed to be adequate techniques for this issue. The experience of each expert was evaluated using the proximity/distance between the individuals in the group of proposed experts and compared with the number of consensus presented.

We hope to have given an improvement to the optimization o the decision support system used in a maritime disaster context of the Portuguese Navy.

Keywords

Decision support system Expert Delphi method Questionnaire Catastrophe Multidimensional scaling

Notes

Acknowledgements

This work was supported by Portuguese funds through theCenter of Naval Research(CINAV), Portuguese Naval Academy, Portugal andThe Portuguese Foundation for Science and Technology(FCT), through theCenter for Computational and Stochastic Mathematics(CEMAT), University of Lisbon, Portugal, project UID/Multi/04621/2019.

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Copyright information

©作者(年代),在春天独占许可证er Nature Switzerland AG 2022

Authors and Affiliations

  1. 1.CINAV, Center of Naval ResearchNaval Academy, Portuguese NavyAlmadaPortugal
  2. 2.CEMAT, Center for Computational and Stochastic Mathematics, Instituto Superior TécnicoLisbon UniversityLisbonPortugal
  3. 3.UNIDEMI, Department of Mechanical and Industrial Engineering, Faculty of Sciences and TechnologyNew Lisbon UniversityCaparicaPortugal
  4. 4.FCT, Faculty of Sciences and TechnologyNew Lisbon UniversityCaparicaPortugal
  5. 5.ISTAR - ISCTE, Instituto Universitário de LisboaLisbonPortugal

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