Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective
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 whic...
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doaj-7a6130bfcb4a4c51a2a2f99b66d749922020-11-25T02:30:58ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942020-09-014898910.3390/jmmp4030089Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management PerspectiveMareike Dornhöfer0Simon Sack1Johannes Zenkert2Madjid Fathi3Electrical Engineering & Computer Science, Knowledge Based Systems & Knowledge Management, University of Siegen, 57076 Siegen, GermanyElectrical Engineering & Computer Science, Knowledge Based Systems & Knowledge Management, University of Siegen, 57076 Siegen, GermanyElectrical Engineering & Computer Science, Knowledge Based Systems & Knowledge Management, University of Siegen, 57076 Siegen, GermanyElectrical Engineering & Computer Science, Knowledge Based Systems & Knowledge Management, University of Siegen, 57076 Siegen, GermanyThe 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.https://www.mdpi.com/2504-4494/4/3/89multi-agent-systemssmart factorycyber–physical production systemsknowledge management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mareike Dornhöfer Simon Sack Johannes Zenkert Madjid Fathi |
spellingShingle |
Mareike Dornhöfer Simon Sack Johannes Zenkert Madjid Fathi Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective Journal of Manufacturing and Materials Processing multi-agent-systems smart factory cyber–physical production systems knowledge management |
author_facet |
Mareike Dornhöfer Simon Sack Johannes Zenkert Madjid Fathi |
author_sort |
Mareike Dornhöfer |
title |
Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective |
title_short |
Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective |
title_full |
Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective |
title_fullStr |
Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective |
title_full_unstemmed |
Simulation of Smart Factory Processes Applying Multi-Agent-Systems—A Knowledge Management Perspective |
title_sort |
simulation of smart factory processes applying multi-agent-systems—a knowledge management perspective |
publisher |
MDPI AG |
series |
Journal of Manufacturing and Materials Processing |
issn |
2504-4494 |
publishDate |
2020-09-01 |
description |
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. |
topic |
multi-agent-systems smart factory cyber–physical production systems knowledge management |
url |
https://www.mdpi.com/2504-4494/4/3/89 |
work_keys_str_mv |
AT mareikedornhofer simulationofsmartfactoryprocessesapplyingmultiagentsystemsaknowledgemanagementperspective AT simonsack simulationofsmartfactoryprocessesapplyingmultiagentsystemsaknowledgemanagementperspective AT johanneszenkert simulationofsmartfactoryprocessesapplyingmultiagentsystemsaknowledgemanagementperspective AT madjidfathi simulationofsmartfactoryprocessesapplyingmultiagentsystemsaknowledgemanagementperspective |
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