System Evaluation and Learning in Evolvable Production Systems : Preliminary considerations and research directions

Dynamicity and unpredictability related to markets is strongly hardening companies’ mission to follow them and satisfy customer needs mainly due to the lack of adequate engineering mechanisms. These effects are felt more intensively in markets where low volumes and high customisation are needed since this requires constant changes in systems that can range from simple setups to total line re-configuration and re-programing. State of the Art Industrial technology has historically been driven to achieve very efficient and flexible production lines for pre-thought problems; however this technology doesn’t satisfy the needs faced by current production requirements where adaptability and responsiveness are off the essence.The last decade witnessed the advent of Evolvable Production Systems (EPS) and other modern paradigms that offer promising approaches to substitute obsolete production strategies. EPS enhances system re-configurability using process-oriented modularity and multi-agent based distributed control endowing the system with units that are autonomous, self-organizing and functionality-oriented. The aggregation of these independent units will then form a system that with a well-defined system architecture and interactions rules can collaborate to complete production plans and react to unpredictable events without re-programing needs.The complexity associated with combinatorial possibilities of forming a system based in such premises raises the need to study how such system performance can be evaluated and how machine learning can be used to discover best system configurations for specific cases. This thesis goal is to enlighten the relation between EPS characteristics, Evaluation and Learning building the foundations for the achievement of Evaluation and Learning mechanisms that can contribute to better system design and configuration to improve system performance and autonomy, and contribute to a more economical solution.

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