Iterative linear programming methods are proposed for optimum balanced animal diet in this paper. According to ＂wooden bucket theory＂ of the nutritional balance, each nutrient in the feeding standard has equal impor...Iterative linear programming methods are proposed for optimum balanced animal diet in this paper. According to ＂wooden bucket theory＂ of the nutritional balance, each nutrient in the feeding standard has equal importance. It＇s unreasonable to use common goal programming to attach different weighted value to different nutritional parameters. This paper introduces an effective algorithm to deal with this kind of problem. When the permitting cost of livestock ration is given, we can design a ration formula with linear program-this is the first round. Then, according to the differences between the permitting cost and the formula cost gained in the first round, adjust the feeding standard and the feeding raw materials, and conduct the second round of linear programming for ration formula. If there is still a very big difference between the formula cost and the permitting cost, the third round will be taken, and so on. In this iteration course the formula cost gradually approaches the permitting cost. It is the key that the feeding standard and feeding raw materials are modified in each round. This method ensured the nutritive equilibrium with the formulation of least-cost ration. This is an especially important method when the primary goal of the optimization tool is to improve economic and nutritive efficiency.展开更多
文摘Iterative linear programming methods are proposed for optimum balanced animal diet in this paper. According to ＂wooden bucket theory＂ of the nutritional balance, each nutrient in the feeding standard has equal importance. It＇s unreasonable to use common goal programming to attach different weighted value to different nutritional parameters. This paper introduces an effective algorithm to deal with this kind of problem. When the permitting cost of livestock ration is given, we can design a ration formula with linear program-this is the first round. Then, according to the differences between the permitting cost and the formula cost gained in the first round, adjust the feeding standard and the feeding raw materials, and conduct the second round of linear programming for ration formula. If there is still a very big difference between the formula cost and the permitting cost, the third round will be taken, and so on. In this iteration course the formula cost gradually approaches the permitting cost. It is the key that the feeding standard and feeding raw materials are modified in each round. This method ensured the nutritive equilibrium with the formulation of least-cost ration. This is an especially important method when the primary goal of the optimization tool is to improve economic and nutritive efficiency.
