A fishing route optimization decision support system: The case of the tuna purse seiner

Abstract

Fisheries face challenges in improving efficiency and reducing their emission footprint and operating costs. Decision support systems offer an opportunity to tackle such challenges. This study focuses on the dynamic fishing routing problem (DFRP) of a tuna purse seiner from a tactical and operational routing point of view. The tactical routing problem is formalized as the dynamic k-travelling salesperson problem with moving targets and time windows, whereas the operational problem is formulated as the time-dependent shortest path problem. The algorithm proposed to solve this problem, called GA-TDA*, couples a genetic algorithm (GA), which uses problem-dependent operators, with a time-dependent A* algorithm. Using real data from a fishing company, the designed GA crossovers were evaluated along with the trade-off between the combination of the proposed objectives: fuel consumption and probability of high catches. The DFRP was also solved as a real dynamic problem with route updates every time a dFAD was fished. The results obtained by this approach were compared with historical fishing trips, where a potential saving in fuel consumption and time at sea of around 57% and 33%, respectively were shown. The dynamic GA-TDA* shows that a better selection of fishing grounds together with considerations about weather conditions can help industry to mitigate and adapt to climate change while decreasing one of their main operational costs.