Using inductive learning to assess compound feed production in cooperative poultry farms

Production scheduling is one of the most important functions in a production company. As a consequence, in recent decades various methods have been proposed for the modeling and solution of particular scheduling problems. In this context, a special case is that of centralized feed manufacturing plants supplying animal food in a cooperative poultry environment. In this paper, we present the SP4 system, an integrated software environment that combines a statistical method (used to calculate the previous consumption data, mortality indices and feed delivery types), a machine learning method (M5P and IBk models – used to calculate the total amount of feed consumed by type) and an ad hoc algorithm which makes flexible orders for compound feed production forecasting. The data used for this study was provided by a leading Spanish Company (Coren Cooperative) specialized in animal feed production and delivery. Raw data (from the years 2007 and 2008) was built from client orders, company production logs, information about the number of animals at different farms and truck trips to the clients. To ensure that the developed system is able to reproduce acceptable results for the unseeable future, we have evaluated various aggregate measures to forecast error (MSE, MAE, MAPE, ME) during the validation of the models. The results reveal that the proposed system performed well, being able to track the dynamic non-linear trend and seasonality, as well as the numerous interactions between correlated variables.

► Using Inductive Learning to Assess Compound Feed Production.
► Integration of statistical and machine learning methods.
► Ad-hoc algorithm form compound feed production forecasting.
► Dynamic non-linear trend and seasonality of variables.
► Hybrid system for production forecasting and scheduling.