Decentralized and on-the-fly agent-based service reconfiguration in manufacturing systems uri icon

abstract

  • Intelligent manufacturing systems rely on the capability to adapt and evolve to face the volatility of dynamic markets. The complexity of these systems increases with the demand of more customized and quality products, which requires more agile and flexible methods to support the dynamic and on-the-fly system reconfiguration aiming to respond quickly to product changes, by offering more efficient services. In this service-oriented manufacturing context, where process functionalities are modelled as services (e.g., quality control, welding and transportation), the dynamic reconfiguration of the services structure (e.g., in terms of quality, processing time and provided features) assumes a critical role to achieve the referred requirements. Despite the current research efforts, the service reconfiguration approaches usually use reactive event triggers, with decisions coming from a centralized decision-maker and performed manually. This means a lack of dynamic and run-time reconfiguration flexibility by discovering opportunities and needs to change, and, thus, exploring possible actions leading to new and appropriate system configurations. To overcome the mentioned issues, it is essential to provide solutions that answer to when and how to reconfigure a manufacturing system in an integrated, automatic and dynamic manner. For this purpose, this paper introduces an agent-based approach for service reconfiguration in manufacturing systems that allows the identification of opportunities in a pro-active and dynamic manner, and the on-the-fly implementation of new configuration solutions leading to a better production efficiency. The experimental results, using a flexible manufacturing system case study, allowed to verify the feasibility and benefits of the proposed agent-based service reconfiguration solution for competitive and collaborative industrial automation scenarios.

publication date

  • January 1, 2018