Design of a Machine Learning-based Decision Support System for Product Scheduling on Non Identical Parallel Machines

Abstract

Production planning in supply chain management faces considerable challenges due to the dynamics and unpredictability of the production environment. Decision support systems based on the evolution of artificial intelligence can provide innovative solutions. In this paper, an approach based on machine learning techniques to solve the problem of scheduling the production of N products on M non-identical parallel machines is proposed. Using regression and classification models, our approach aims to predict overall production costs and assign products to the right machines. Some experiments carried out on simulated data sets demonstrate the relevance of the proposed approach. In particular, the XGBoost model stands out for its superior performance compared with the other tested ML algorithms. The proposed approach makes a significant contribution to the optimization of production scheduling, offering significant potential for improvement in Supply Chain Management.