An AI Based Online Scheduling Controller for Highly Automated Production Systems

  • Emanuele Carpanzano
  • Amedeo Cesta
  • Fernando Marinò
  • Andrea Orlandini
  • Riccardo Rasconi
  • Anna Valente
Conference paper
Part of the Lecture Notes in Production Engineering book series (LNPE)


Highly automated production systems are conceived to efficiently handle evolving production requirements. This concerns any level of the system from the configuration and control to the management of production. The proposed work deals with the production scheduling level. The authors present an AI-based online scheduling controller for Reconfigurable Manufacturing Systems (RMSs) whose main advantage is its capacity of dynamically interpreting and adapting any production anomaly or system misbehavior by regenerating on-line a new schedule. The performance of the controller has been assessed by running a set of closed-loop experiments based on a real-world industrial case study. Results demonstrate that the capability of automatically synthesizing plans together with recovery actions severely contribute to ensure a high and continuous production rate.


Production Schedule Constraint Satisfaction Problem Recovery Action Control Architecture Reconfigurable Manufacture System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research presented in the current work has been partially funded under the Regional Project “CNR - Lombardy Region Agreement: Project 3”. Cesta and Rasconi acknowledge the partial support of MIUR under the PRIN project 20089M932N (funds 2008).


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Emanuele Carpanzano
    • 1
  • Amedeo Cesta
    • 2
  • Fernando Marinò
    • 2
  • Andrea Orlandini
    • 1
  • Riccardo Rasconi
    • 2
  • Anna Valente
    • 1
  1. 1.MilanoItaly
  2. 2.CNR-National Research Council of ItalyISTCRomaItaly

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