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Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms

Author

Listed:
  • Manuel Luna

    (University of Cantabria)

  • Ignacio Llorente

    (University of Cantabria)

  • Angel Cobo

    (University of Cantabria)

Abstract

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.

Suggested Citation

  • Manuel Luna & Ignacio Llorente & Angel Cobo, 2022. "Determination of feeding strategies in aquaculture farms using a multiple-criteria approach and genetic algorithms," Annals of Operations Research, Springer, vol. 314(2), pages 551-576, July.
    • Handle: RePEc:spr:annopr:v:314:y:2022:i:2:d:10.1007_s10479-019-03227-w
    DOI: 10.1007/s10479-019-03227-w
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    References listed on IDEAS

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    1. Bjorndal, Trond & Lane, Daniel E. & Weintraub, Andres, 2004. "Operational research models and the management of fisheries and aquaculture: A review," European Journal of Operational Research, Elsevier, vol. 156(3), pages 533-540, August.
    2. JosÉ Figueira & Salvatore Greco & Matthias Ehrogott, 2005. "Multiple Criteria Decision Analysis: State of the Art Surveys," International Series in Operations Research and Management Science, Springer, number 978-0-387-23081-8, April.
    3. A. Cobo & I. Llorente & L. Luna, 2015. "Swarm Intelligence in Optimal Management of Aquaculture Farms," International Series in Operations Research & Management Science, in: Lluis M. Plà-Aragonés (ed.), Handbook of Operations Research in Agriculture and the Agri-Food Industry, edition 127, chapter 0, pages 221-239, Springer.
    4. Yoram Wind & Thomas L. Saaty, 1980. "Marketing Applications of the Analytic Hierarchy Process," Management Science, INFORMS, vol. 26(7), pages 641-658, July.
    5. Wang, Ling & Chu, Jian & Wu, Jun, 2007. "Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process," International Journal of Production Economics, Elsevier, vol. 107(1), pages 151-163, May.
    6. El-Gayar, Omar F. & Leung, PingSun, 2001. "A multiple criteria decision making framework for regional aquaculture development," European Journal of Operational Research, Elsevier, vol. 133(3), pages 462-482, September.
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