Comparison of Linear and Nonlinear Programming Techniques for Animal Diet

Pratiksha Saxena

doi:10.5923/j.am.20110102.17

Abstract

Linear programming techniques have been extensively used for animal diet formulation for more than last fifty years. To overcome the drawback of linear approximation of objective function for diet formulation, a mathematical model based on nonlinear programming technique is proposed to measure animal performance in terms of milk yield and weight gain. At the second step, it compares the result of proposed program with that of linear programming model. Result of proposed model gives better results using nonlinear programming. Thus the study is an attempt to develop a nonlinear programming model for optimal planning and best use of nutrient ingredients.

Highlights

Research on nutrition is under process for more than hundred years
Diet formulated by linear programming is based on assumption of linearity between animal yield and nutrient ingredients included in the diet
This study compares the method of linear and non-linear programming of animal diet formulation and shows that linear programming method gives result at corner points of feasible area

Summary

Introduction

Diet formulation is a process by which different ingredients are combined to provide necessary nutrition to animals at different stages of production. Diet formulated by linear programming is based on assumption of linearity between animal yield and nutrient ingredients included in the diet. To overcome the assumption of linearity and to include complexity of different nutrient ingredients, a nonlinear model is proposed in this paper to maximize milk yield. This concept of non-linear programming may be used to maximize the weight gain of the animal or animal yields approximately. Present study is carried out to compare the diet formulation model by linear and nonlinear programming method. This result is compared to the result of linear programming model

Material and Methods

Formulation of Nonlinear Programming Model

Result and Discussion

Linear Programming Formulation

Conclusions