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It describes how the urge towards digitalisation of manufacturing in the context of the 4th Industrial revolution has shaped simulation in the design and operation of manufacturing systems and reviews the new approaches that have arisen in the literature.
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This paper investigates the major historical milestones in the evolution of manufacturing systems simulation technologies and examines recent industrial and research approaches in key fields of manufacturing. It allows experimentation and validation of product, process, and system design and configuration. Simulation comprises an indispensable set of IT tools and methods for the successful implementation of digital manufacturing. Manufacturing systems simulation has proven to be a powerful tool for designing and evaluating a manufacturing system due to its low cost, quick analysis, low risk and meaningful insight that it may provide, improving thus the understanding of the influence of each component. The remaining unfulfilled key enablers are then the subject of an extensive discussion on the remaining research perspectives on holonic architectures needed to achieve a complete support of Industry 4.0.Īs the industrial requirements change at a rapid pace due to the drastic evolution of technology, the necessity of quickly investigating potential system alternatives towards a more efficient manufacturing system design arises more intensely than ever. After the presentation of the holonic paradigm and holon properties, this article highlights how historical and current holonic control architectures can partly fulfil Industry 4.0 key enablers. During the last 20 years, the holonic paradigm has become a major paradigm of Intelligent Manufacturing Systems. From most relevant articles extracted from existing literature, a list of 10 key enablers for Industry 4.0 is first presented.
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Over-connectivity and data management abilities targeted by Industry 4.0 paradigm enable the emergence of more flexible and reactive control systems, based on the cooperation of autonomous and connected entities in the decision-making process. The flexibility claimed by the next generation production systems induces a deep modification of the behaviour and the core itself of the control systems. Experimentations and results show competitive performance and highlight interesting research directions. Data analytics tools are developed to help manufacturing system Experts define and fine-tune rules, based on rule firing statistics and corresponding context indicators and performance assessment acquired from simulation.
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SP agents use context indicators and performance assessment to activate Expert rules and update AHP preferences and scales before making dispatching control decisions to react to disturbances. A multi-agent discrete event simulation model is used to create a set of normal and disturbed production scenarios. The mechanism is applied to a dispatching problem in an industrial scale assembly process. The control mechanism involves an Analytic Hierarchy Process (AHP) augmented with Expert rules to cope with the limitations of standard AHP in dealing with dynamic problems.
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This article suggests a PDC approach that enables SPs to learn how to make control decisions to react to disturbances and maintain continuity of operations. Nevertheless, developing Product-Driven Control (PDC) mechanisms that enable Smart Products (SPs) to make control decisions to cope with disturbances is still a complex, open-ended, and challenging problem. In Cyber-Physical Manufacturing Systems (CPMS), numerous distributed control architectures were suggested to make different production entities active with respect to decision-making and control processes, so that they can process information, interact, and make control decisions in an autonomous and adaptive way.