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Multiple-objective (goal) programming model for feed formulation: an example for reducing nutrient variation.

F Zhang1, W B Roush

  • 1Department of Poultry Science, The Pennsylvania State University, University Park 16802-3501, USA.

Poultry Science
|March 5, 2002
PubMed
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A multiple-objective programming (MOP) model offers a flexible approach to broiler feed formulation, balancing ration cost and nutrient variance effectively. This method provides better compromise solutions compared to traditional linear programming for complex animal nutrition challenges.

Area of Science:

  • Animal Nutrition
  • Operations Research
  • Computational Biology

Background:

  • Traditional feed formulation often relies on linear programming, which can struggle with multiple, conflicting objectives.
  • Optimizing animal feed involves balancing cost, nutrient requirements, and ingredient restrictions simultaneously.
  • Minimizing nutrient variance is crucial for consistent animal growth and health.

Purpose of the Study:

  • To develop and evaluate a multiple-objective programming (MOP) model for broiler grower feed formulation.
  • To compare the flexibility and efficiency of MOP against traditional linear programming in feed optimization.
  • To assess the MOP model's ability to handle conflicting objectives and provide compromise solutions.

Main Methods:

  • A multiple-objective programming (MOP) model was constructed using Microsoft Excel Solver for broiler rations (3-6 weeks).

Related Experiment Videos

  • The model incorporated 21 ingredients and 17 nutrients, with amino acids based on digestible values.
  • Soft constraints included meeting nutrient requirements, ingredient restrictions, and nutrient ratios; hard constraints focused on least-cost and minimal nutrient variances (protein, methionine, lysine).

    • Main Results:

    • The MOP model demonstrated greater flexibility in finding compromise solutions compared to traditional linear programming.
    • MOP successfully handled multiple conflicting objectives simultaneously, a limitation of single-objective linear programming.
    • The MOP model provided optimal compromise solutions, satisfying decision-makers by balancing ration cost with minimum variances in protein and methionine.

    Conclusions:

    • Multiple-objective programming (MOP) is an efficient tool for complex feed formulation, offering superior flexibility over linear programming.
    • MOP effectively manages trade-offs between competing objectives like cost and nutrient variability in animal feed.
    • The MOP model aids decision-making by integrating multiple criteria and facilitating interaction with stakeholders.