Animal Feed Optimization under Price Fluctuations using Evolutionary Algorithms

In the livestock industry feed cost impacts overall production cost, as the cost of feed amounts to over 60% of the production costs. This makes feed formulation of utmost concern for many breeders. Various challenges including ingredient short-age, and ingredient price fluctuations are encountered during the feed formulation process. In this work, using evolutionary algorithm, the feed formulation problem is modified to include feed cost variation that models feed ingredient price fluctuations, to minimize the feed cost per month. The objective function is modified by generating synthetic ingredient price from real-world price data. A 20% standard deviation is used to generate 12 different costs representing the cost for each month in the year. The proposed method incorporates possible price variations to search for optimal solutions in providing adequate feed materials that minimizes the cost for each month, and can select unique feed materials for each month that fits the animals growth stage and nutritional requirements.