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dc.contributor.authorGranado, Igor-
dc.contributor.authorSzlapczynska, Joanna-
dc.contributor.authorSzlapczynski, Rafal-
dc.contributor.authorHernando, Leticia-
dc.contributor.authorFernandes-Salvador, Jose A.-
dc.date.accessioned2026-04-20T14:14:14Z-
dc.date.available2026-04-20T14:14:14Z-
dc.date.issued2025-06-30-
dc.identifier.citationApplied Soft Computing Journal, (2025), 182, 113587es_ES
dc.identifier.urihttp://dspace.azti.es/handle/24689/2785-
dc.description.abstractThis study aims to enhance economic and environmental sustainability of fisheries through fishing routing methods that can reduce operational costs, emission footprints, and incidental fishing risks. To achieve this, a novel problem definition is introduced, the time-dependent multi-objective orienteering problem with time windows and moving targets (TDMOOP-TWMT). Unlike existing fishing routing problems, the TDMOOP-TWMT allows users to define their fishing trips by setting a maximum time at sea rather than a predefined number of fishing sets. This multi-objective problem includes three goals: fuel-oil consumption, catches of tuna species, and incidental catches of non-target species (bycatch). To address this problem, the w-MOEA/D algorithm is employed, which incorporates decision-makers' preferences using wide weight intervals for each objective, eliminating the need for precise weight values. Compared to the classical MOEA/D, the w-MOEA/D method achieves solutions closer to the true Pareto front while reducing the final solution set based on users' preferences. To demonstrate the potential application and benefits in a real context, 12 historical routes are employed across different fishing scenarios, each defined by varying the weight intervals of the objectives. The results show that w-MOEA/D routes allow for consuming less fuel and catching more tuna, though with a higher risk of bycatch when compared to historical trips. However, prioritizing bycatch avoidance reduces this risk while maintaining similar fuel efficiency, although with a lower increase in catches. In summary, this study highlights the effectiveness of the proposed solution method in supporting fishers' decision-making by incorporating their preferences when planning fishing routes.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.titleEvolutionary multi-objective fishing routing with decision maker's preferenceses_ES
dc.typeArticlees_ES
dc.identifier.journalApplied Soft Computing Journales_ES
dc.format.volume182es_ES
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