Giving the ratification of the ballast water management convention, a number of ballast water treatment technologies are emerging in the maritime industry; however, choosing the most appropriate technology has been a big challenge for ship owners because it is a very complex and critical task. The aim of this article is to propose a multi-attribute decision analysis model which integrates the fuzzy analytical hierarchy process and the weighted linear combination method into a more comprehensive approach to select the most appropriate ballast water treatment technology for ship owners. The proposed model consists of 6 attributes (i.e. technological readiness, commercial readiness, operational readiness, seafarer’s skills readiness, biological efficacy readiness and cost of ballast water treatment technologies) and 17 ballast water treatment technologies among which ship owners can choose the best. The fuzzy analytical hierarchy process is used to obtain more realistic weights of the attributes, while the weighted linear combination is applied to prioritize the technologies. To demonstrate the applicability of the model, it is applied to select the best ballast water treatment technology for a ship owner in Ghana. The result shows that the ballast water treatment technology which uses membrane filter is selected as the best. Sensitivity analysis is then carried out to determine the effect of changing attributes’ weights on the final priority rankings of the ballast water treatment technologies. Finally, the model is validated by comparing the obtained results to those from the PROMETHEE model and both results are consistent.


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    Titel :

    A fuzzy analytical hierarchy process–weighted linear combination decision-making model for prioritization of ballast water treatment technologies by ship owners in Ghana


    Beteiligte:
    Animah, Isaac (Autor:in)


    Erscheinungsdatum :

    01.11.2019


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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