Job shop smart manufacturing scheduling by deep reinforcement learning for industry 4.0

• Autores: Julio C. Serrano Ruiz
• Directores de la Tesis: Raúl Poler Escoto (dir. tes.), Josefa Mula Bru (dir. tes.)
• Lectura: En la Universitat Politècnica de València ( España ) en 2024
• Idioma: inglés
• Tribunal Calificador de la Tesis: Rafael Pastor Moreno (presid.), Ángel Alejandro Juan Pérez (secret.), Miguel Ortega-Mier (voc.)
• Programa de doctorado: Programa de Doctorado en Ingeniería y Producción Industrial por la Universitat Politècnica de València
• Materias:
  • Matemáticas
    • Ciencia de los ordenadores
      • Inteligencia artificial
      • Sistemas de control de producción
    • Probabilidad
      • Procesos de Markov
  • Ciencias tecnológicas
    • Tecnología industrial
      • Procesos industriales
• Enlaces
  • Tesis en acceso abierto en: RiuNet
• Resumen

  • The Industry 4.0 (I4.0) paradigm relies, to a large extent, on the potential of information and communication technologies (ICT) to improve the competitiveness and sustainability of industries. The smart manufacturing scheduling (SMS) concept arises and draws inspiration from this potential. As a digital transformation strategy, SMS aims to optimise industrial processes through the application of technologies, such as the digital twin (DT), the zero-defect manufacturing (ZDM) management model and deep reinforcement learning (DRL), for the ultimate purpose of guiding operations scheduling processes towards real-time adaptive automation and to reduce disturbances in production systems.

    SMS is based on four design principles of the I4.0 spectrum: automation, autonomy, real-time capability and interoperability. Based on these key principles, SMS combines the capabilities of the DT technology to simulate, analyse and predict; with the ZDM model, to prevent disturbances in production planning and control systems; by the DRL modelling approach, to improve real-time decision making. This joint approach orients operations scheduling processes towards greater efficiency and, with it, a better performing and more resilient production system.

    This research firstly undertakes a comprehensive review of the state of the art on SMS. By taking the review as a reference, the research proposes a conceptual model of SMS as a digital transformation strategy in the job shop scheduling process context. Finally, it proposes a DRL-based model to address the implementation of the key elements of the conceptual model: the job shop DT and the scheduling agent. The algorithms that integrate this model have been programmed in Python and validated against several of the most well-known heuristic priority rules. The development of the model and algorithms is an academic and managerial contribution in the production planning and control area.