An AI Based Online Scheduling Controller for Highly Automated Production Systems Emanuele Carpanzano, Amedeo Cesta, Fernando Marinò, Andrea Orlandini, Riccardo Rasconi and Anna Valente Abstract 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 sche- dule. 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. E. Carpanzano  A. Orlandini  A. Valente (&) Via Bassini 15, 20133 Milano, Italy e-mail: anna.valente@itia.cnr.it E. Carpanzano e-mail: emanuele.carpanzano@itia.cnr.it A. Orlandini e-mail: andrea.orlandini@itia.cnr.it Amedeo. Cesta  F. Marinò  R. Rasconi CNR-National Research Council of Italy, ISTC, Via S. Martino della Battaglia 44, 00185 Roma, Italy e-mail: amedeo.cesta@istc.cnr.it F. Marinò e-mail: fernando.marino@istc.cnr.it R. Rasconi e-mail: riccardo.rasconi@istc.cnr.it K. Windt (ed.), Robust Manufacturing Control, Lecture Notes in Production Engineering, DOI: 10.1007/978-3-642-30749-2_8, Ó Springer-Verlag Berlin Heidelberg 2013 105