Del Gallo, M.; Mazzuto, G.; Ciarapica, F.E.; Bevilacqua, M. Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review. Electronics2023, 12, 4732.
Del Gallo, M.; Mazzuto, G.; Ciarapica, F.E.; Bevilacqua, M. Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review. Electronics 2023, 12, 4732.
Del Gallo, M.; Mazzuto, G.; Ciarapica, F.E.; Bevilacqua, M. Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review. Electronics2023, 12, 4732.
Del Gallo, M.; Mazzuto, G.; Ciarapica, F.E.; Bevilacqua, M. Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review. Electronics 2023, 12, 4732.
Abstract
This systematic literature review explores the burgeoning use of Artificial Intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. In this landscape, AI is crucial in addressing the complexity and dynamism of contemporary manufacturing processes, including machine breakdowns, fluctuating orders and unpredictable job arrivals. This systematic literature review, conducted using the Scopus database and bibliometric tools, pursues two primary objectives. First, it identifies the prevailing trends in solving scheduling problems with AI and identifies the most commonly used AI techniques in the literature. Secondly, it analyses how authors have successfully employed AI to address production scheduling challenges in real-world industrial settings and assesses the benefits obtained by companies. The dynamic nature of manufacturing systems requires adaptive scheduling paradigms. AI, including Particle Swarm Optimization, Neural Networks, and Reinforcement Learning, is applied to optimize production processes, predict machine failures, and achieve substantial benefits. In real-world applications, these AI-driven solutions have led to reduced production costs, enhanced energy efficiency, and more efficient scheduling processes. AI is increasingly recognized as an essential tool in addressing the evolving challenges of modern manufacturing environments.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
