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Proceedings of GO 2005, pp. 257 – 262.Global optimisation applied to pig nutritionGraham R. Wood, 1 Duangdaw Sirisatien, 1 and Patrick C.H. Morel 21 Department of Statistics, Macquarie University, NSW 2109, Australia, gwood@efs.mq.edu.au, dsirisat@efs.mq.edu.au2 Institute of Food, Nutrition and Human Health, Massey University, Palmerston North, New Zealand,P.C.Morel@massey.ac.nzAbstractKeywords:Global optimisation techniques can be used to determine how to feed animals in order to maximiseprofitability, so extending the way in which linear programming has been used to find minimumcost diets. Keys to the extension are recently developed animal growth models and algorithms fornonlinear optimisation. The problem is described, a solution is presented and the nature of theobjective function is explored.Feed cost, genetic algorithm, gross margin, pig genotype, weaner cost1. IntroductionEfficient animal production is of critical importance on our increasingly finite planet. Formany decades, linear programming has been used to determine minimum cost animal diets,based on a range of feedstuffs, their cost, their composition and dietary constraints. With theadvent of animal growth models (see, for example [2]) and efficient nonlinear optimisationalgorithms, it is now possible to extend this traditional use of optimisation to determine afeeding schedule which maximises profitablity [1]. The purpose of this paper is to describe,from a global optimisation perspective, how this is carried out.The main findings to date of the research are i) that efficient optimisation methods areneeded to find practical and useful feeding schedules and ii) that optimal feeding schedulescan differ from the “feed-to-lean" schedules [4] generally used in the industry. The approach islimited by our ability to accurately measure needed on-farm growth model parameters (suchas maximum protein deposition levels) and the accuracy of the growth models themselves.Nevertheless, trials have been conducted in New Zealand, based on the outcomes of the optimisation,in an attempt to improve production efficiency.The paper is organised as follows. In the next section we describe the objective function tobe maximised and its domain. In Section 3 we discuss algorithms used to find the objectivefunction optimum. The nature of the objective function is explored in Section 4 and an ideafor an improved algorithm proposed. The paper concludes with a summary.2. The domain and the objective functionIn this section the domain of the objective function and the objective function itself are described.