Simulation-based Data Analysis to Support the Planning of Flexible Manufacturing Systems

Filz, Marc-André; Herrmann, Christoph; Thiede, Sebastian

doi:10.11128/sne.30.tn.10531

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…Specific technologies such as RFID (Radio-Frequency Identification) tagging are seen as one specific enabling component of flexible manufacturing, with regard to scheduling of jobs [69], whereby parts are encoded with assembly instructions and ready by machines installed on automated production lines. Much flexible manufacturing research is still focussed on the optimization of production scheduling through the use of machine-learning algorithms [69][70][71][72].…”

Section: Flowshopmentioning

confidence: 99%

“…Maganha et al [90] state that the design of a flexible manufacturing implementation should be seen as a cyclical process that evolves over time rather than a one-off activity. The need for virtual representations of reconfigurable manufacturing systems was made by [72], who introduced the concept of a digital avatar to represent a manufacturing layout detailing individual production machines. As with other manufacturing methodologies, the rise of personalised production is a further stimulus for research into reconfigurability, along with the potential of digital technologies [91][92][93][94].…”

Section: Reconfigurable Manufacturing Systemmentioning

confidence: 99%

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Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems

Turner

Oyekan

2023

Sustainability

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This paper provides a classification of manufacturing types in terms of new technological tools provided in the Industry 5.0 framework. The manufacturing types agile, holonic, flexible and reconfigurable benefit from and are potentially changed by Industry 4.0 technologies and the human-centric focus of Industry 5.0. Furthermore, the use of Lifecycle Analysis (LCA) provides a holistic method for estimating the true value of emissions emitted during the carrying out of manufacturing decisions. As a result, LCA may be used as a central guiding framework, in addition to the use of Circular Economy metrics, for decisions in manufacturing whose results could be presented to humans as part of a scenario-generation system using visualisations within a Digital Twin environment. This enables a decision maker to make informed decisions regarding current and future production needs. Regardless of the size of production facility, this integrated approach is perhaps the most significant gap in research identified by this survey of manufacturing types and systems when viewed through the lens of Industry 5.0. This paper makes the contribution of providing an assessment of the major manufacturing types in the context of Industry 5.0, highlighting the gaps in the current research and providing a sustainable and human-centric agenda supported by LCA use with modern production methodologies.

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Section: Flowshopmentioning

confidence: 99%

Section: Reconfigurable Manufacturing Systemmentioning

confidence: 99%

Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems

Turner

Oyekan

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…2019 Filz, Herrmann and Thiede [49] Investigation of the application of an agent-based simulation for material supply chains inside a "matrix-structured manufacturing system". Simulation factors such as required new materials per station, transport of produced goods, usage of automated guided vehicles or locations for supplies needed to be considered leading to a simulation of uncertain and dynamic material supply strategies.…”

Section: Year Authors Approachmentioning

confidence: 99%

Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective

Dornhöfer

Sack

Zenkert

et al. 2020

JMMP

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The implementation of Industry 4.0 and smart factory concepts changes the ways of manufacturing and production and requires the combination and interaction of different technologies and systems. The need for rapid implementation is steadily increasing as customers demand individualized products which are only possible if the production unit is smart and flexible. However, an existing factory cannot be transformed easily into a smart factory, especially not during operational mode. Therefore, designers and engineers require solutions which help to simulate the aspired change beforehand, thus running realistic pre-tests without disturbing operations and production. New product lines may also be tested beforehand. Data and the deduced knowledge are key factors of the said transformation. One idea for simulation is applying artificial intelligence, in this case the method of multi-agent-systems (MAS), to simulate the inter-dependencies of different production units based on individually configured orders. Once the smart factory is running additional machine learning methods for feedback data of the different machine units may be applied for generating knowledge for improvement of processes and decision making. This paper describes the necessary interaction of manufacturing and knowledge-based solutions before showing an MAS use case implementation of a production line using Anylogic.

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Analysis of metaheuristic optimisation techniques for simulated matrix production systems

Benfer,

Heyer,

Brützel

et al. 2023

Prod. Eng. Res. Devel.

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Increasing demand for individualised products has led to the concept of mass customisation, combining high product variety with production efficiency coming along with mass production. Companies are moving to matrix production systems with complex product flows for mass customisation. One challenge in such systems is the determination of optimal system configurations to fulfil future demands while minimising production costs. An approach to determine the ideal configuration is to use metaheuristics like genetic algorithms or simulated annealing to optimise simulation models. However, it is unclear which methods are ideally suited to finding the best solutions. This contribution compares the performance of genetic algorithms and simulated annealing when optimising the configuration of a company-specific matrix production system using discrete event simulation. The methods are evaluated using different objective functions. For the genetic algorithm, different observation strategies are also tested. Overall, the simulated annealing approach delivers better results with shorter solution times. The contributing factors leading to the different results are discussed, and areas for future research are pointed out.

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Simulation-based Data Analysis to Support the Planning of Flexible Manufacturing Systems

Cited by 5 publications

References 11 publications

Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems

Manufacturing in the Age of Human-Centric and Sustainable Industry 5.0: Application to Holonic, Flexible, Reconfigurable and Smart Manufacturing Systems

Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective

Analysis of metaheuristic optimisation techniques for simulated matrix production systems

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