Research Article Open Access

A Multi-Criteria Proximal Bundle-based Optimization Approach to Chick-Mash Feed Formulation

Abstract

The development of feed formulation is essentially a problem of optimization which involves selecting the best alternative, starting from a specified set of possibilities. This study is aimed at developing a generic decision supporting system for optimizing the poultry feed production through the application of multi-objective proximal bundle approach, taking into consideration the energy optimization, limiting the amino acid variation and providing a least cost of production. A non-differentiable interactive multi-objective bundle-based optimization method was used in solving this problem. This technique provided a wide range of alternatives choices for the decision maker to formulate an effective and optimum feed that will minimize the costs, achieve more balanced ration, limit the methionine variation for growth, and optimize the metabolized energy based on feed at his/her disposal. The algorithm of this method is based on the objective functions classification. According to this classification, a new (multi-objective) optimization problem was formed and solved by a Multi-objective Proximal Bundle method. The method in turn generated different alternative formulations from which the decision maker arrived at the final decision. The results were displayed as value path according to their range of values, and from the lists of alternatives, it is clear that none of the alternatives can be better improved without impairing others. At this point the decision maker will now make a choice from the list, based on his preference. This is done by ranking the three objectives according to the decision maker’s order of preference. The decision maker must therefore be willing to sacrifice something. This work therefore provided a platform to provide solution to the problem of conflicting objectives of energy optimization, limiting amino acid variation and ration cost minimization in feed formulation.

Olalere Olusegun Abayomi, Alara Oluwaseun Ruth, Mohamed Saad Bala, Mohamed Farag Twibi

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