Multiobjective stochastic programming for feed formulation

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 mo...

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Bibliographic Details
Published in:The Journal of the Operational Research Society Vol. 60; no. 12; pp. 1738 - 1748
Main Authors: Peña, T, Lara, P, Castrodeza, C
Format: Journal Article
Language:English
Published: London Taylor & Francis 01.12.2009
Palgrave Macmillan
Palgrave Macmillan UK
Taylor & Francis Ltd
Subjects:
agriculture
Animals
Applied sciences
Approximation
Business and Management
Decision theory. Utility theory
Diet
Dietary minerals
Economic costs
Exact sciences and technology
Feed formulation
Feed science
Feeds
Fish byproducts
Ingredients
Linear programming
Management
Mathematical programming
Minimization of cost
multi-objective fractional programming
Normal distribution
Nutrients
Nutritional requirements
Operational research and scientific management
Operational research. Management science
Operations research
Operations Research/Decision Theory
Probability
Random variables
Safety factors
Standard deviation
Stochastic models
stochastic programming
Studies
Swine
Theoretical Paper
Theoretical Papers
agriculture
multi-objective fractional programming
stochastic programming
Stochastic model
Probabilistic approach
Decision making
Variability
Multiobjective programming
Machine feed
Linear programming
Modeling
Linear model
Stochastic programming
Diet
Preference
Agriculture
Fractional programming
Rational function
ISSN:0160-5682, 1476-9360
Online Access:Get full text
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Summary: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.
Bibliography:SourceType-Scholarly Journals-1
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content type line 14
ISSN:0160-5682
1476-9360
DOI:10.1057/jors.2008.106