Performance Assessment in Self-organising Mechatronic Systems: A First Step towards Understanding the Topology Influence
The research and development of self-organising mechatronic systems has been a hot topic in the past 10 years which conducted to very promising results in the close past. The proof of concept attained in IDEAS project [1] that plug&produce can be achi
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KTH Royal Institute of Technology, Department of Production Engineering, Stockholm, Sweden 2 CTS UNINOVA, Dep. de Eng. Electrotecnica, F.C.T. Universidade Nova de Lisboa, Monte da Caparica, Portugal {pmsn2,onori}@kth.se, {ldr,jab}@uninova.pt
Abstract. The research and development of self-organising mechatronic systems has been a hot topic in the past 10 years which conducted to very promising results in the close past. The proof of concept attained in IDEAS project [1] that plug&produce can be achieved in these systems opens up new research horizons on the topics of system design, configuration and performance evaluation. These topics need to consider that the systems are no longer static prototypes but instead several distributed components that can be added and removed in runtime. The distribution of modules in the system and their inherent connections will then potentially affect the system’s global behaviour. Hence it is vital to understand the impact on performance as the system endures changes that affect its topology. This article presents an exploratory test case that shows that as a system evolves (and the nature of the network of its components changes) the performance of the system is necessarily affected in a specific direction. This performance landscape is necessarily complex and very likely nonlinear. Simulation plays therefore an important role in the study of these systems as a mean to generate data that can be later on used to generate macro level knowledge that may act as a guideline to improve both design and configuration. Keywords: Multi-Agent Systems, Performance Assessment, Evolvable Production Systems, Self-Organizing Systems, Simulation.
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Introduction
Assembly lines are flow oriented production systems which were originally built for cost-efficient mass production. Given the current turbulent markets they are now struggling to cope with constant re-design and re-configurations imposed by high customisation and low volumes [2]. As the business paradigm shifts towards an increased customization and personalization the requirements imposed on production change [3], [4]. Traditional systems present optimal solutions for particular forecasted products. However, they fail to present the desired agility to follow volatile market L.M. Camarinha-Matos et al. (Eds.): DoCEIS 2014, IFIP AICT 423, pp. 75–84, 2014. © IFIP International Federation for Information Processing 2014
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needs. This augments the need of systems that are rapidly deployable, reconfigurable and autonomous and that can accommodate the required changes with minimal integration/programming effort and therefore maximum cost-effectiveness [5]. Modern production paradigms emerged in the last two decades aiming at offering responsive and cost effective solutions. Some examples are Bionic Manufacturing Systems [6], Reconfigurable Manufacturing Systems [7], Evolvable Production Systems (EPS) [8], Holonic Manufacturing Systems [9] and Changeable Manufacturing Systems [10]. They all share some core principles such as
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