Abstract
This Chapter Reports On A Real-Life Implementation Of A Planning-Cum-Scheduling System In A Discrete-Continuous Industry That Produces Rolled Steel Wire And Cable Of Different Qualities And Specifications. The Chapter Describes In Some Detail The Industrial Environment Of The Application, The Experimentation That Was Conducted Prior To Implementation In Order To Tune The Various Parameters Of The Decision-Support System, And The Operational Problems That Arise Which Demand Careful Planning As Well As Dynamic Corrective Action. We Present A Multi-Model Approach That Combines Optimization Modules With Heuristic, Meta-Heuristic, Simulation And Multi-Criteria Modules In A Coherent Planning And Scheduling System Together With A Novel Architecture Of The Information-Decision Process. A Description Of The Different Models Collaborating At Different Decision Levels Is Also Given. The Implementation Of The Information/Control System Together With The Results Obtained To Date From The System Is Discussed. We Highlight The Advantages And Limitations Of The Current Version Of The System Design And Conclude With The Current Avenues Of Investigation, Which We Are Pursuing In Cooperation With The Enterprise, And The Anticipated Future Developments.
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Notes
- 1.
For the French ‘planification’, meaning ‘planning’, and ‘ordonnancement’ meaning ‘scheduling’.
- 2.
For the French ‘Petit Toron’.
- 3.
For the French ‘Moyen Toron’.
- 4.
For the French ‘Gros Toron’.
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Acknowledgment
This chapter reports on two projects supported by the Walloon Region of Belgium and the European Social Fund, which the authors acknowledge with gratitude. The authors thank the staff at Fontainunion for their invaluable cooperation and contribution to this work.
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Artiba, A., Dhaevers, V., Duvivier, D., Elmaghraby, S.E. (2011). A Multi-Model Approach for Production Planning and Scheduling in an Industrial Environment. In: Kempf, K., Keskinocak, P., Uzsoy, R. (eds) Planning Production and Inventories in the Extended Enterprise. International Series in Operations Research & Management Science, vol 152. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-8191-2_19
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