This paper describes the development of a multiagent system (MAS) to support the implementation of zero-defect manufacturing strategies in multi-stage production systems. The MAS infrastructure, combined with on-line inspection tools, data analytics and knowledge generation, constitutes a suitable approach to integrate process and quality control in multi-stage environments. This will allow the early detection of product defects, the adaptation to operating condition changes and the optimisation of manufacturing processes. This type of integrated management structure is aligned with a zero-defect manufacturing production model which is of paramount importance in the actual state-of-the-art manufacturing paradigms. As a proof of concept, the devised manufacturing supervision model was deployed into an experimental multi-stage system that run a set of several tests on electrical motors. The agent-based solution was implemented using the JADE framework and the exchange of information structured by proper data models and industrial based Internet-of-Things and Machine-to-Machine technologies, such as OPC-UA, REST and JSON. The obtained results demonstrate the suitability of the devised integrated management model as a vehicle to achieve dynamic and continuous system improvement in multi-stage manufacturing environments.

This paper provides an overview of the actual developments on smart grids, particularly analysing the efforts on research and development in the different regions in the world. The paper also discusses the roles of Artificial Intelligence (AI) and Information & Communication Technologies (ICT) in the future development of smart grids. Particularly, it highlights the importance of power simulation tools in developing control strategies and discusses how AI techniques can work with these tools to provide simulation solutions that are closer to a real implementation.

Self-sustainability of micro grids is an important challenge to be addressed, particularly when they operate disconnected from the utility grid. The multi-agent systems paradigm allows implementing intelligent and predictive strategies towards an efficient management of the power flow. In this paper, an agent-based model is introduced, optimizing the load demand, through load prioritization, by shedding and rescheduling, and using forecasting mechanisms to improve the micro grids self-sustainability. The designed agent-based model and strategies were implemented and tested in the Repast agent-based modelling and simulation framework. Experimental results show improvements in the self-sustainability of the micro grid under analysis when operating in critical mode.

The continuous change in the manufacturing world is demanding more flexible, responsive and accurate planning tools, which are able to assist the decision-makers to take tactical and strategic decisions on short notice with a high level of confidence. For this purpose, these tools should dynamically explore different operative scenarios in the planning procedure and produce information about key performance indicators. This paper describes the development of an agent-based strategic planner, combining the flexibility of multi-agent systems principles with the optimization capability of a Mixed Integral Programming technique. The tool is integrated in an ecosystem of heterogeneous decision-making systems through an Enterprise Service Bus that also provides access to legacy data.