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<p>The ongoing evolution of industrial systems, driven by Industry 4.0 technologies and increasing uncertainty in production environments, has significantly complicated the coordination of production and maintenance activities. In response to these challenges, research on scheduling problems has expanded considerably in recent years. This paper provides a structured review of the main scheduling models proposed for different workshop configurations, including flow shop, job shop, and open shop settings, with a particular focus on maintenance-related constraints. The study examines and compares the principal solution approaches reported in the literature, ranging from classical optimization techniques to advanced heuristic and metaheuristic algorithms. Special attention is given to recent contributions that incorporate predictive maintenance and artificial intelligence–based methods in order to enhance decision-making in smart and connected manufacturing systems. Finally, a comparative analysis is presented to identify the key criteria influencing the choice of scheduling methods, with the objective of achieving efficient solutions while maintaining acceptable computational effort.</p>

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Modeling and optimization approaches for joint maintenance scheduling in the context of industry 4.0

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Karrach Ghizlane,
  • Jeffali Faouaz,
  • Sadiqi Assia,
  • Bouchnaif Jamal,
  • El Barkany Abdellah

摘要

The ongoing evolution of industrial systems, driven by Industry 4.0 technologies and increasing uncertainty in production environments, has significantly complicated the coordination of production and maintenance activities. In response to these challenges, research on scheduling problems has expanded considerably in recent years. This paper provides a structured review of the main scheduling models proposed for different workshop configurations, including flow shop, job shop, and open shop settings, with a particular focus on maintenance-related constraints. The study examines and compares the principal solution approaches reported in the literature, ranging from classical optimization techniques to advanced heuristic and metaheuristic algorithms. Special attention is given to recent contributions that incorporate predictive maintenance and artificial intelligence–based methods in order to enhance decision-making in smart and connected manufacturing systems. Finally, a comparative analysis is presented to identify the key criteria influencing the choice of scheduling methods, with the objective of achieving efficient solutions while maintaining acceptable computational effort.