Abstract
The paper introduces a new approach on design of distributed manufacturing control systems. It will be shown how multi-agent principles, self-learning, self-adaptation and a new engineering method for distributed production systems can be used to enhance integration of production control process quality control. It will be highlighted how multi-agent systems may be implemented to enforce interaction of manufacturing execution system and distributed control system, enhancing the exploitation of all available information at the quality control and process control levels. Additional focus is set on production process modeling, quality control algorithms and an engineering methodology integrating all these aspects within. The final results are validated in the field of white ware using a washing machine production line.

Abstract
Cyber-physical systems are an emergent paradigm to design complex, adaptive and smart systems, combining computational applications with physical hardware devices. Multi-agent systems play an important role in such systems to provide flexibility, robustness and adaptation, but their alignment will require the integration of agents with physical devices. This process is usually complex and time consuming due to the proprietary protocols provided by the hardware automation devices. This paper describes the deployment of an agent-based system in a small-scale flexible production system composed by a set of heterogeneous automation devices, such as programmable logic controllers and robots.

Abstract
This paper proposes the impact assessment of the jobs order in the optimal time of operations in a Flexible Job Shop Scheduling Problem. In this work a real assembly cell was studied: the AIP-PRIMECA cell at the Universite de Valenciennes et du Hainaut-Cambresis, in France, which is considered as a Flexible Job Shop problem. The problem consists in finding the machines operations schedule, taking into account the precedence constraints. The main objective is to minimize the batch makespan, i.e. the finish time of the last operation completed in the schedule. Shortly, the present study consists in evaluating if the jobs order affects the optimal time of the operations schedule. The genetic algorithm was used to solve the optimization problem. As a conclusion, it's assessed that the jobs order influence the optimal time.

Abstract
Nowadays, manufacturing control systems have evolved from reactive and informative decision support system to a proactive and intelligent manufacture management mechanism. Industries require both optimal and adaptive manufacturing processes in order to respond competitively to market requirements. In response, advanced manufacturing control systems are configured as artificial intelligence distributed architectures capable to support environment disturbances. However, these techniques, specifically Multi-Agent systems and Holonic Manufacturing Systems, are weak supporting optimal performance. Conversely, Operational Research decision support systems achieve optimality under centralized architectures. Still, these are weak supporting adaptable processes under environmental disturbances. Consequently, researchers recently have focused in articulating optimality and adaptation paradigms in order to construct a robust optimal-wise and adaptable mechanism, constructs a proposed typology according structural features and discusses future possibilities for balancing optimality and adaptability characteristics.

Abstract
Multi-agent systems (MAS) paradigm is a suitable approach to implement distributed manufacturing systems addressing the emergent requirements of flexibility, robustness and responsiveness. In such systems, the distributed agents have a local view of the system environment being the global data collection a complex and critical issue to provide the functionalities of the ISA-95 standard, such as dynamic scheduling, maintenance management and quality assurance. This paper describes the data collection mechanisms in a multi-agent system that supports the global monitoring over the time and the trend analysis to perform self-adaptation and self-optimization. The proposed approach was developed and tested in an industrial washing machines production line.

Abstract
This paper introduces the multi-agent systems paradigm and presents some industrial applications of this AI approach, namely in manufacturing, handling and logistics domains. The road-blockers for the current weak adoption of this technology in industry are also discussed, and finally the current trends and several future challenges are pointed out to increase the wider dissemination and acceptance of the multi-agent technology in industry.