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A modular framework for optimal scheduling of industrial processes

Ein modularer Ansatz für die optimale Ausführungsplanung in industriellen Produktionsprozessen
  • Axel Schild

    Axel Schild received his Dr.-Ing. degree in Control Engineering in 2011 from Ruhr-Universität Bochum, Germany. Since then, he has held several industrial Senior Expert positions for advanced and predictive control across different industry sectors and applications. In this role, he initiated, coordinated and contributed to various publically-funded industrial-academic research consortia. His research activities focus on transfering latest-edge research results in the area of optimization and data-science into industrially-viable control solutions for energy and the automotive applications.

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    , Alexander Rose

    Alexander Rose received his master’s degree in Mechatronics from Leibniz Universität Hannover in 2018, Germany. He is a PhD student at the Faculty of Mechanical Engineering at the Leibniz University Hannover and a member of the Advanced Control working group at the Institute of Engineering Design, Mechatronics and Electromobility at the Hannover University of Applied Sciences and Arts, Germany. His current research interests include development and application of optimal scheduling for energy intensive batch processes and data-based prediction of process variables with Long Short-Term Memory networks.

    , Martin Grotjahn

    Martin Grotjahn received his Dr.-Ing. degree in the field of robotic control in 2003 from Leibniz University Hannover, Germany. Afterwards he led a team in the automotive industry that was responsible for the development of longitudinal dynamic control systems. In 2009, he became professor for Mechatronics at Hannover University of Applied Sciences and Arts, where he has held the position of Vice President for Research since 2020, following various leading positions in the Faculty of Mechanical Engineering and Bioprocess Engineering. He also heads the Advanced Control working group at the Institute of Engineering Design, Mechatronics and Electromobility. His main research interest is the transfer of control theory into real-world applications, e. g., in the fields of drive and automation technology, production technology and energy management of buildings.

    and Bennet Luck

    Bennet Luck received his Dr.-Ing. degree in Control Engineering in 2016 from Gottfried Wilhelm Leibniz Universität Hannover, Germany. He currently heads the Control and Virtual Design Cluster at IAV. In this role, he initiated, coordinated and contributed to several publically-funded projects covering applied data science and advanced control solutions for industry processes and the applications in the energy sector.

Abstract

This paper proposes an extended Petri net formalism as a suitable language for composing optimal scheduling problems of industrial production processes with real and binary decision variables. The proposed approach is modular and scalable, as the overall process dynamics and constraints can be collected by parsing of all atomic elements of the net graph. To conclude, we demonstrate the use of this framework for modeling the moulding sand preparation process of a real foundry plant.

Zusammenfassung

Dieser Beitrag schlägt einen erweiterten Petrinetz-Formalismus vor, der für die Zusammenstellung optimaler Planungsprobleme von vielfältigen industriellen Produktionsprozessen mit realen und binären Entscheidungsvariablen geeignet ist. Der vorgeschlagene Ansatz ist modular und skalierbar, da die Gesamtprozessdynamik und -beschränkungen durch Analyse und Betrachtung aller atomaren Elemente des Netzgraphen aufgestellt werden können. Der Ansatz wird abschließend für die Komposition des Planungsproblems des Formsandaufbereitungsprozesses einer realen Gießereianlage eingesetzt.

About the authors

Axel Schild

Axel Schild received his Dr.-Ing. degree in Control Engineering in 2011 from Ruhr-Universität Bochum, Germany. Since then, he has held several industrial Senior Expert positions for advanced and predictive control across different industry sectors and applications. In this role, he initiated, coordinated and contributed to various publically-funded industrial-academic research consortia. His research activities focus on transfering latest-edge research results in the area of optimization and data-science into industrially-viable control solutions for energy and the automotive applications.

Alexander Rose

Alexander Rose received his master’s degree in Mechatronics from Leibniz Universität Hannover in 2018, Germany. He is a PhD student at the Faculty of Mechanical Engineering at the Leibniz University Hannover and a member of the Advanced Control working group at the Institute of Engineering Design, Mechatronics and Electromobility at the Hannover University of Applied Sciences and Arts, Germany. His current research interests include development and application of optimal scheduling for energy intensive batch processes and data-based prediction of process variables with Long Short-Term Memory networks.

Martin Grotjahn

Martin Grotjahn received his Dr.-Ing. degree in the field of robotic control in 2003 from Leibniz University Hannover, Germany. Afterwards he led a team in the automotive industry that was responsible for the development of longitudinal dynamic control systems. In 2009, he became professor for Mechatronics at Hannover University of Applied Sciences and Arts, where he has held the position of Vice President for Research since 2020, following various leading positions in the Faculty of Mechanical Engineering and Bioprocess Engineering. He also heads the Advanced Control working group at the Institute of Engineering Design, Mechatronics and Electromobility. His main research interest is the transfer of control theory into real-world applications, e. g., in the fields of drive and automation technology, production technology and energy management of buildings.

Bennet Luck

Bennet Luck received his Dr.-Ing. degree in Control Engineering in 2016 from Gottfried Wilhelm Leibniz Universität Hannover, Germany. He currently heads the Control and Virtual Design Cluster at IAV. In this role, he initiated, coordinated and contributed to several publically-funded projects covering applied data science and advanced control solutions for industry processes and the applications in the energy sector.

References

1. Doganis, P. and H. Sarimveis. 2007. Optimal scheduling in a yogurt production line based on mixed integer linear programming. Journal of Food Engineering 80(2): 445–453.10.1016/j.jfoodeng.2006.04.062Search in Google Scholar

2. Karoui, O., Z. Li, N. Wu, M. Khalgui, E. A. Nasr and A. M. El-Tamimi. 2018. One-step control-ahead approach for the design of an optimal Petri-net based deadlock prevention policy. IEEE Access 6: 34307–34323.10.1109/ACCESS.2018.2843380Search in Google Scholar

3. Kondili, E., C. Pantelides and R. Sargent. 1993. A general algorithm for short-term scheduling of batch operations – i. milp formulation. Computers & Chemical Engineering 17(2): 211–227.10.1016/0098-1354(93)80015-FSearch in Google Scholar

4. Lunze, J. 2017. Ereignisdiskrete Systeme. De Gruyter Oldenbourg.10.1515/9783110484717Search in Google Scholar

5. Méndez, C., G. Henning and J. Cerdá. 2001. An milp continuous-time approach to short-term scheduling of resource-constrained multistage flowshop batch facilities. Computers & Chemical Engineering 25(4-6): 701–711.10.1016/S0098-1354(01)00671-8Search in Google Scholar

6. Méndez, C. A., J. Cerdá, I. E. Grossmann, I. Harjunkoski and M. Fahl. 2006. State-of-the-art review of optimization methods for short-term scheduling of batch processes. Computers & chemical engineering 30(6-7): 913–946.10.1016/j.compchemeng.2006.02.008Search in Google Scholar

7. Narahari, Y. and N. Viswanadham. 1985. A Petri net approach to the modelling and analysis of flexible manufacturing systems. Annals of operations research 3(8): 449–472.10.1007/BF02023780Search in Google Scholar

8. Rose, A., M. Grotjahn, A. Schild and B. Luck. 2021. Application of feedback-corrected optimal scheduling for reducing the energy consumption of a mixing process in foundry. In: Proceedings of IEEE Intern. Conf. on systems theory, control and computing.10.1109/ICSTCC52150.2021.9607108Search in Google Scholar

9. Rose, A., A. Schild, B. Luck and M. Grotjahn. 2021. Optimal control with linear integer programming for reducing the energy consumption of interdependent mixing machines in foundry. In: 2021 22nd IEEE International Conference on Industrial Technology (ICIT), vol. 1. IEEE, pp. 1138–1143.10.1109/ICIT46573.2021.9453606Search in Google Scholar

10. Ye, J., Z. Li and A. Giua. 2014. Decentralized supervision of Petri nets with a coordinator. IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(6): 955–966.Search in Google Scholar

11. Zhou, M. 1998. Modeling, analysis, simulation, scheduling, and control of semiconductor manufacturing systems: A Petri net approach. IEEE Transactions on Semiconductor Manufacturing 11(3): 333–357.10.1109/66.705370Search in Google Scholar

Received: 2021-09-16
Accepted: 2021-11-19
Published Online: 2022-01-13
Published in Print: 2022-01-27

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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