Long‐Term Forecasting Method in the Supply Chain Based on an Artificial Neural Network with Multi‐Agent Metaheuristic Training

Abstract

The problem of increasing the efficiency of long-term forecasting in the supply chain is examined. Neural network forecasting methods that are based on reservoir calculations, which increases the forecast accuracy, are proposed. Methods for identifying parameters of forecast models based on the metaheuristics are proposed for the methods mentioned above. These methods were researched on the basis of the data from the logistics company Ekol Ukraine and are intended for intelligent computer-based supply chain management systems.