Abstract
Petri net is a mathematical model for representing parallel, asynchronous, and distributed systems. Petri nets can model parallel and synchronous activities in manufacturing systems at various levels of abstraction. In this study, we propose data-driven modeling and scheduling for cellular manufacturing systems using process mining with Petri nets. In the proposed method, the event log data is extracted from a virtual plant and then the Petri net model considering the movement of products and operators is developed by using the process mining technique with the Petri net model. We also derived an approximate solution for the derived Petri net model from the event log using a local search method using a Petri net simulator. The analysis and modification of the model are conducted in the proposed method. Near-optimal schedules are derived using Petri net simulations. The validity of the proposed model is evaluated.
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References
Liu, C., Lian, J., Yin, Y., Li, W.: Seru seisan - an innovation of the production management mode in Japan. Asian J. Technol. Innov. 18(2), 89–113 (2010)
Cecil, J.A., Srihari, K., Emerson, C.R.: A review of Petri-net applications in manufacturing. Int. J. Adv. Manuf. Technol. 7(3), 168–177 (1992)
Grobelna, I., Karatkevich, A.: Challenges in application of Petri nets in manufacturing systems. Electronic 10(18), 2305 (2021)
Alhourani, F.: Cellular manufacturing system design considering machines reliability and parts alternative process routings. Int. J. Prod. Res. 54(3), 846–863 (2016)
Süer, G.A., Ates, O.K., Mese, E.M.: Cell loading and family scheduling for jobs with individual due dates to minimise maximum tardiness. Int. J. Prod. Res. 52(19), 5656–5674 (2014)
Ferro, R., Cordeiro, G.A., Ordóñez, R.E.C., Beydoun, G., Shukla, N.: An optimization tool for production planning: a case study in a textile industry. Appl. Sci. 11(18), 8312 (2021)
Kloud, T., Koblasa, F.: Solving job shop scheduling with the computer simulation. Int. J. Transp. Logist. 20(11), 7–17 (2018)
Haraszkó, C., Németh, I.: DES configurators for rapid virtual prototyping and optimization of manufacturing systems. Periodica Polytechnica Mech. Eng. 59(3), 143–152 (2015)
Van Der Aalst, W.M.P.: Process Mining: Data Science in Action, Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-49851-4
Berti, A., Zelst, S.V., Schuster, D.: PM4Py: a process mining library for Python. Softw. Impacts 17, 100556 (2023)
Acknowledgments
The authors would like to thank the funding provided by JSPS KAKENHI KIBAN (B) 23K22983.
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Kurakado, H., Nishi, T., Liu, Z. (2024). Data-Driven Scheduling of Cellular Manufacturing Systems Using Process Mining with Petri Nets. In: Thürer, M., Riedel, R., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Production Management Systems for Volatile, Uncertain, Complex, and Ambiguous Environments. APMS 2024. IFIP Advances in Information and Communication Technology, vol 729. Springer, Cham. https://doi.org/10.1007/978-3-031-65894-5_2
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DOI: https://doi.org/10.1007/978-3-031-65894-5_2
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