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Applications of artificial intelligence to support the decision-making process of seabream and seabass aquaculture companies [abstract]

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  • Decision-making in aquaculture is becoming increasingly complex due to the need of taking into account large volumes of data, and to consider other aspects besides the economic ones, such as product quality or the environmental sustainability, which are sometimes opposing and difficult to integrate. However, the development of simulation and optimization methods that support decision making in the strategic and operational planning in fish farming has received less attention. The present work explains the development of a novel methodology, based on the integration of multiple-criteria techniques and population-based metaheuristics, which allows decision-makers to align its production objectives to their preferences in multiple aspects and thus determine the best strategy. In this case, the methodology is applied to gilthead seabream production, which together with European seabass, is the main species in Mediterranean aquaculture and the second in the EU in terms of value. Despite the positive evolution of production, companies´ profitability in the EU has suffered high volatility and the evolution of production efficiency has not been as positive as in other industries. The results obtained show the utility of this methodology for integrating numerous objectives and prove that in most cases these techniques improve company's results. Therefore, we may conclude that this methodology will improve the management capacity of aquaculture producers and their understanding of the performance of the main production variables. This research was undertaken under the MedAID project, which has received funding from the European Union's Horizon 2020 Research and Innovation Programme. Grant agreement no 727315 (
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  • Vigo, Galicia, Spain
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