As the dairy products have a short consumption period, the accurate prediction of their demand is very important for the dairy industry. Accordingly, this research specifically addresses the prediction of dairy product demand (DPD). The main contribution of this research is to provide an integrated framework based on statistical tests, time-series prediction and artificial intelligence with the runner-root algorithm (RRA) as a novel meta-heuristic algorithm to obtain the best prediction of DPD in Iran. First, a series of economic and social indicators that seemed to be effective in the demand for dairy products are identified and the ineffective indices are eliminated. Next, the artificial intelligence tools including MLP, ANFIS, and LSTM are implemented and improved with the help of RRA. The designed hybrid methods are implemented by using data from 2013 to 2017 of the Iran diary industry. This novel algorithm is compared to gray wolf optimization, invasive weed optimization, and particle swarm optimization. The results show that the proposed MLP-RRA has the most ability to improve by using meta-heuristic algorithms. The coefficient of determination is 98.19%. Moreover, in each artificial intelligence tools, RRA causes better results than the other tested algorithms. The highly accurate results confirm that the proposed hybrid methods based on the RRA algorithm are able to improve the prediction of demand for various products.
An Improved Artificial Intelligence Based on Gray Wolf Optimization and Cultural Algorithm to Predict Demand for Dairy Products: A Case Study
