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Multiobjective stochastic programming for feed formulation

Author

Listed:
  • T Peña

    (University of Valladolid)

  • P Lara

    (University of Córdoba)

  • C Castrodeza

    (University of Valladolid)

Abstract

The minimum cost linear programming model used traditionally for feed formulation does not take account of variability of nutrients in feed ingredients. Therefore, it may be that the nutrient requirements of the animal are not adequately met. In this paper, we show how a multiobjective stochastic model that permits confronting the cost of the ration with the probabilities of meeting the nutrient requirements of the animal can enhance the process of animal diet formulation. The model presented here does not require any a priori information from the decision maker, eliciting his preferences through an interactive process. This is the main advantage in relation to other models found in the literature for treating the problem of nutrient variability, which introduce stochastic constraints in the single objective minimum cost model requiring fixing the level of probability desired for each one of the nutrients in advance.

Suggested Citation

  • T Peña & P Lara & C Castrodeza, 2009. "Multiobjective stochastic programming for feed formulation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1738-1748, December.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:12:d:10.1057_jors.2008.106
    DOI: 10.1057/jors.2008.106
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    References listed on IDEAS

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    2. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.
    3. Rosshairy Abd. Rahman & Graham Kendall & Razamin Ramli & Zainoddin Jamari & Ku Ruhana Ku-Mahamud, 2017. "Shrimp Feed Formulation via Evolutionary Algorithm with Power Heuristics for Handling Constraints," Complexity, Hindawi, vol. 2017, pages 1-12, November.

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