Abstract
The multi-agent systems paradigm, enhanced with biological inspired concepts, such as swarm intelligence and self-organization, offers an alternative way to design control solutions that address the current demands for flexible, robust, responsive and adaptive manufacturing and automation systems. Being the development of these systems traditionally complex, the use of modelling and simulating environments may contribute for the fast prototyping and proof of concept of these solutions. This paper explores the use of these environments and uses the NetLogo platform to illustrate the development of an agent-based control application for an experimental production system. The designed and tested agent-based system exhibits robustness and adaptation to changing environments based on the stigmergy concept.

Abstract
Global markets impose strong requirements to manufacturing domain in terms of flexibility, robustness and reconfigurability. The multi-agent systems (MAS) paradigm is suitable to handle such requirements, introducing an alternative way to design complex, agile and adaptive systems. However, MAS based solutions may suffer of myopia due to the local optimal decision-making performed by the autonomous distributed agents having a partial knowledge of the problem. This paper depicts the optimization problem in MAS, particularly having in mind the achievement of adaptation, and explores the contributions that biology can offer to handle this issue. Two bio-inspired MAS solutions for routing pallets in a real assembly system are described to illustrate how optimization and adaptation can be combined.

Abstract
Multi-agent systems (MAS) are driving the way to design and engineer control solutions that exhibit flexibility, adaptation and reconfigurability, which are important advantages over traditional centralized systems. The understanding, design and testing of such distributed agent-based approaches, and particularly those exhibiting self-* properties, are usually a hard task. Simulation assumes a crucial role to analyse the behaviour of MAS solutions during the design phase and before its deployment into the real operation. Particularly, Agent-Based Modelling (ABM) tools are well suited to simulate MAS systems that exhibit complex phenomena, like emergent behaviour and self-organization. This paper discusses the simulation of agent-based manufacturing systems and introduces the advantages of using ABM tools. The NetLogo platform is used to illustrate the benefits of such tools in the manufacturing world on the specification of a MAS system for a washing machine production line.

Abstract
Traditional centralized manufacturing structures were found inadequate to face the challenging requirements of flexibility and re-configurability. Lately, several manufacturing paradigms were introduced to face this challenge, being unified in the objective of providing decentralized control over distributed entities. In spite of their potential benefits, some important questions are far from been answered, namely how control structures are dynamically formed and evolved and how to combine adaptation and optimization. This paper introduces the main principles for re-configurable manufacturing systems that answers to these questions, based on the ADACOR holonic architecture and incorporating mechanisms inspired in other areas of science, notably biology, nature, theory of complexity and artificial life.

Abstract
Traditional manufacturing solutions, based on centralized structures, are ineffective in unpredictable and volatile scenarios. Recent manufacturing paradigms, such as Ho Ionic Manufacturing Systems, handle better these unpredictable situations but aren't able to achieve the performance optimization levels displayed by the classical centralized solutions when the system runs without perturbations. This paper introduces a holonic manufacturing architecture that considers biological insights, namely emergence and self-organization, to achieve adaptation and responsiveness without degrading the performance optimization. For this purpose, self-organization and self-learning mechanisms embedded at micro and macro levels play an important role, as well the design of stabilizers to control the system nervousness in such dynamic and adaptive behaviour.

Abstract
New production control paradigms, such as holonic and multi-agent systems, allow the development of more flexible and adaptive factories. In these distributed approaches, autonomous entities possess a partial view of the environment, being the decisions taken from the cooperation among them. The introduction of self-organization mechanisms to enhance the system adaptation may cause the system instability when trying to constantly adapt their behaviours, which can drive the system to fall into a chaotic behaviour. This paper proposes a nervousness control mechanism based on the classical Proportional, Integral and Derivative feedback loop controllers to support the system self-organization. The validation of the proposed model is made through the simulation of a flexible manufacturing system.