An integrated system solution for supply chain optimization in the chemical process industry |
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Authors: | Guido Berning Marcus Brandenburg Korhan Gürsoy Vipul Mehta Franz-Josef Tölle |
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Affiliation: | (1) Bayer AG, Bayer Technology Services, Supply Chain Optimization, 51368 Leverkusen, Germany (e-mail: {guido.berning.gb, marcus.brandenburg.mb, korhan.guersoy.kg, vipul.mehta.vm, franz-josef.toelle.ft}@bayer-ag.de) , DE |
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Abstract: | This paper considers a complex scheduling problem in the chemical process industry involving batch production. The application
described comprises a network of production plants with interdependent production schedules, multi-stage production at multi-purpose
facilities, and chain production. The paper addresses three distinct aspects: (i) a scheduling solution obtained from a genetic algorithm based optimizer, (ii) a mechanism for collaborative planning among the involved plants, and (iii) a tool for manual updates and schedule changes. The tailor made optimization algorithm simultaneously considers alternative
production paths and facility selection as well as product and resource specific parameters such as batch sizes, and setup
and cleanup times. The collaborative planning concept allows all the plants to work simultaneously as partners in a supply
chain resulting in higher transparency, greater flexibility, and reduced response time as a whole. The user interface supports
monitoring production schedules graphically and provides custom-built utilities for manual changes to the production schedule,
investigation of various what-if scenarios, and marketing queries.
RID="*"
ID="*" The authors would like to thank Hans-Otto Günther and Roland Heilmann for helpful comments on draft versions of this
paper. |
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Keywords: | :Supply chain management – APS-system – Collaborative planning – Optimization – Genetic algorithm |
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