Artificial Intelligence (AI) is used to help the tuna fishing industry to improve its day by day operations at sea. Tuna fishing vessels that fish with FADs (Fish Aggregating Devices) face an optimization problem in a dynamic scenario never seen before in other industries. Solving this issue can help this industry to minimize fuel consumption and emissions to the atmosphere. Considering the optimization challenge in greater detail, the problems to solve are two. The first is the basic case in which a tuna fishing vessel equipped with N buoys or FADs wants to know the best route to visit them all. The second goes further and tries to reach the same solution when a group of M vessels shares N FADs. In this second case, a more global solution is needed, including multiple vessels and more FADs to visit, but it can solve the global optimization problem for an entire fleet of tuna fishing vessels, with optimal results. The combination of AI algorithms and prediction is key to finding a solution for such a complex, dynamic environment. Specifically, a genetic algorithm (GA) is combined with a prediction method. This is an academic novelty and gives excellent results in comparison with the current industry standard and with the literature covering the best techniques for solving this problem. Moreover, the solution suggested is a new kind of global solution that is valid for both static and dynamic scenarios. The proposed solution is tested using real data from the fleet. Results show how profitable it is for the firm, as well as for the planet, greatly reducing emissions to the atmosphere and helping the tuna fishing industry to be more sustainable.
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