Evolutionary multi-objective fishing routing with decision maker’s preferences - Granado, Igor; Szlapczynska, Joanna; Szlapczynski, Rafal; Hernando, Leticia; Fernandes-Salvador, Jose A. (Applied soft computing, 2025)
This 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. •Presents multi-objective fishing routing with economic and environmental goals.•Equivalent to MOO time-dependent orienteering problem with time windows and moving targets.•Utilizes user-provided weight intervals instead of precise weight values per objective.•w-MOEA/D algorithm finds solutions closer to the true Pareto Front than original MOEA/D.•Routes with lower fuel usage and trade-offs between catches and bycatch are obtained.