This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.

Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review / Del Gallo, M.; Mazzuto, G.; Ciarapica, F. E.; Bevilacqua, M.. - In: ELECTRONICS. - ISSN 2079-9292. - ELETTRONICO. - 12:23(2023). [10.3390/electronics12234732]

Artificial Intelligence to Solve Production Scheduling Problems in Real Industrial Settings: Systematic Literature Review

Del Gallo M.
;
Mazzuto G.;Ciarapica F. E.;Bevilacqua M.
2023-01-01

Abstract

This literature review examines the increasing use of artificial intelligence (AI) in manufacturing systems, in line with the principles of Industry 4.0 and the growth of smart factories. AI is essential for managing the complexities in modern manufacturing, including machine failures, variable orders, and unpredictable work arrivals. This study, conducted using Scopus and Web of Science databases and bibliometric tools, has two main objectives. First, it identifies trends in AI-based scheduling solutions and the most common AI techniques. Second, it assesses the real impact of AI on production scheduling in real industrial settings. This study shows that particle swarm optimization, neural networks, and reinforcement learning are the most widely used techniques to solve scheduling problems. AI solutions have reduced production costs, increased energy efficiency, and improved scheduling in practical applications. AI is increasingly critical in addressing the evolving challenges in contemporary manufacturing environments.
2023
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/325371
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