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Web MCA-based Decision Support System for Incident Situations in Maritime Traffic: Case Study of Adriatic Sea

Published online by Cambridge University Press:  21 June 2017

Nenad Mladineo ,
Marko Mladineo  and
Snjezana Knezic
Nenad Mladineo*
Affiliation:
(University of Split, Faculty of Civil Engineering, Architecture and Geodesy (Matice hrvatske 15, 21000 Split, Croatia)
Marko Mladineo
Affiliation:
(University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (Rudera Boskovica 32, 21000 Split, Croatia)
Snjezana Knezic
Affiliation:
(University of Split, Faculty of Civil Engineering, Architecture and Geodesy (Matice hrvatske 15, 21000 Split, Croatia)
*
(E-mail: [email protected])
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Abstract

This paper describes a Multi-Criteria-Analysis (MCA)-based Decision Support System (DSS) developed for the management of incidents in maritime traffic. The developed DSS helps to organise a large quantity of information related to emergency management, spatial data and “live” data (radar data, weather forecasting data), to make it available to decision makers in a comprehensible and user-friendly way. Special care has been taken to model human Decision-Making (DM) processes during incident situations. Since the DM process is always multi-criterial, a Multi-Criteria Decision-Making (MCDM) method called Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE) is used. However, a simplified variation of PROMETHEE II has been utilised to make results more understandable to non-expert users. The aim of this research is to incorporate effective DSS in human DM processes, thus reducing the possibility of making poor decisions. The concept of Web MCA-based DSS is presented as a case study: Web DSS developed for the east coast of the Adriatic Sea.

Keywords

Marine TrafficOperations ResearchSafetyMapping
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 6 , November 2017 , pp. 1312 - 1334
Copyright
Copyright © The Royal Institute of Navigation 2017 

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