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MasDISPO_xt: heat and sequence optimisation based on simulated trading inside the supply chain of steel production
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International Conference on Autonomous Agents archive
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track table of contents
Estoril, Portugal
SESSION: Manufacturing and logistics table of contents
Pages 23-26  
Year of Publication: 2008
Authors
Sven Jacobi  German Research Center for Artificial Intelligence, Stuhlsatzenhausweg, Saarbrrücken
David Raber  German Research Center for Artificial Intelligence, Stuhlsatzenhausweg, Saarbrrücken
Klaus Fischer  German Research Center for Artificial Intelligence, Stuhlsatzenhausweg, Saarbrrücken
Sponsors
ACM: Association for Computing Machinery
AAAI : Association for the Advancement of Artifical Intelligence
Publisher
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ABSTRACT

The production of steel normally constitutes the inception of most Supply Chains in different areas. Steel manufacturing companies are strongly affected by bull whip effects. Due to nondeterministic incoming orders and changes of customer requirements on accepted orders, making the right decision at a certain stage can be the difference between earning or loosing a great turnover. Improving their operational efficiency is required to keep a competitive position on the market. Therefore, flexible planning and scheduling systems are needed to support these processes which are based on considerable amounts of data which can hardly be processed manually. Existing systems are dominated by centralized decision making processes, mostly data driven and often not modeling the business processes they should. MasDISPO_xt is an agent-based generic online planning and online scheduling system for monitoring of the complete Supply Chain of Saarstahl AG, a globally respected steel manufacturer. This paper concentrates on the creation and optimisation of heats and sequences as a presetting for the production inside the steelwork.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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S. Jacobi, E. Leon-Soto, C. Madrigal-Mora, and K. Fischer. Masdispo: A multiagent decision support system for steel production and control. AAAI Innovative Applications of Artificial Intelligence, 2007.
 
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Collaborative Colleagues:
Sven Jacobi: colleagues
David Raber: colleagues
Klaus Fischer: colleagues