Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms
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Since the 1990s, fishing production has stagnated and aquaculture has experienced an exponential growth thanks to the production on an industrial scale. One of the major challenges facing aquaculture companies is the management of breeding activity affected by biological, technical, environmental and economic factors. In recent years, decision-making has also become increasingly complex due to the need for managers to consider aspects other than economic ones, such as product quality or environmental sustainability. In this context, there is an increasing need for expert systems applied to decision-making processes that maximize the economic efficiency of the operational process. One of the production planning decisions more affected by these changes is the feeding strategy. The selection of the feed determines the growth of the fish, but also generates the greatest impact of the activity on the environment and determines the quality of the product. In addition, feed is the main production cost in finfish aquaculture. In order to address all these problems, the present work integrates a multiple-criteria methodology with a genetic algorithm that allows determining the best sequence of feeds to be used throughout the fattening period, depending on multiple optimization objectives. Results show its utility to generate and evaluate different alternatives and fulfill the initial hypothesis, demonstrating that the combination of several feeds at precise times may improve the results obtained by one-feed strategies.
KeywordsAquaculture management Operational research Genetic algorithms Multiple-criteria Decision-making Feeding strategies
This paper is part of the MedAID project which has received funding from the European Union’s H2020 program under Grant Agreement 727315. The authors also wish to thank the Ibero-American Program for the Development of Science and Technology, CYTED, and the Red Iberoamericana BigDSSAgro (Ref. P515RT0123) for their support of this work.
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