Daily Forecasting of Demand Orders With Optimal Architecture Artificial Neural Network Learning Models

In recent years, with the increase in volume of buying orders, demand forecast based on the order data is important for improvement of production, distribution and selling services. For this reason, the predictability of orders will increase efficiency in many areas by timely delivering orders, increasing earnings, and customer satisfaction in trading. This article aims to estimate total amount of daily orders by using an optimal structured artificial neural network learning model. To optimize rectangular architecture of artificial neural network model, a metaheuristic optimization, which determines the number of hidden layers and number of neurons, is used. In the study, training of neural networks was carried out with the Levenberg-Marquardt backpropagation algorithm for daily orders collected for 60 days. During this training, the network's layer and neuron number were optimized with a gray wolf optimization algorithm. Results indicate that optimal architecture neural network can better estimate total daily demand orders.