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Erschienen in:

15.03.2022

Digital twin-driven dynamic scheduling of a hybrid flow shop

verfasst von: Khalil Tliba, Thierno M. L. Diallo, Olivia Penas, Romdhane Ben Khalifa, Noureddine Ben Yahia, Jean-Yves Choley

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 5/2023

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Abstract

Industries require, nowadays, to be more adaptable to unforeseen real-time events as well as to the rapid evolution of their market (e.g. multiplication of customers, increasingly personalized and unpredictable demand, etc.). To meet these challenges, manufacturers need new solutions to update their production plan when a change in the production system or its environment occurs. In this context, our research work deals with a dynamic scheduling problem of a real Hybrid Flow Shop considering the specific constraints of a perfume manufacturing company. This paper proposes a Digital Twin-driven dynamic scheduling approach based on the combination of both optimization and simulation. For the optimization, we have developed a mixed integer linear programming (MILP) scheduling model taking into account the main specific scheduling requirements of our case study. Regarding the simulation approach, a 3D shop floor model has been developed including the additional stochastic aspects and constraints which are difficult or impossible to model with a MILP approach. These two models are connected with the real shop floor to create a digital twin (DT). The developed DT allows the re-scheduling of production according to internal and external events. Finally, validation scenarios on a perfume case study have been designed and implemented in order to demonstrate the feasibility and the relevance of the proposed digital twin-driven dynamic scheduling approach.

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Abdelilah, B., El Korchi, A., & Balambo, M. A. (2018). Flexibility and agility: Evolution and relationship. Journal of Manufacturing Technology Management, 29(7), 1138–1162. https://doi.org/10.1108/JMTM-03-2018-0090CrossRef

Abedinnia, H., Glock, C. H., & Schneider, M. D. (2017). Machine scheduling in production: A content analysis. Applied Mathematical Modelling, 50, 279–299. https://doi.org/10.1016/j.apm.2017.05.016CrossRef

Aheleroff, S., Zhong, R. Y., & Xu, X. (2020). A digital twin reference for mass personalization in industry 4.0. Procedia CIRP, 93, 228–233. https://doi.org/10.1016/j.procir.2020.04.023CrossRef

Allahverdi, A., Gupta, J. N. D., & Aldowaisan, T. (1999). A review of scheduling research involving setup considerations. Omega, 27(2), 219–239. https://doi.org/10.1016/S0305-0483(98)00042-5CrossRef

Barrett, R. T., & Barman, S. (1986). A SLAM II simulation study of a simplified flow shop. Simulation, 47(5), 181–189. https://doi.org/10.1177/003754978604700502CrossRef

Baykasoğlu, A., & Ozsoydan, F. B. (2018). Dynamic scheduling of parallel heat treatment furnaces: A case study at a manufacturing system. Journal of Manufacturing Systems, 46, 152–162. https://doi.org/10.1016/j.jmsy.2017.12.005CrossRef

Behnamian, J. (2014). Scheduling and worker assignment problems on hybrid flowshop with cost-related objective function. The International Journal of Advanced Manufacturing Technology, 74(1), 267–283. https://doi.org/10.1007/s00170-014-5960-yCrossRef

Bennell, J. A., Mesgarpour, M., & Potts, C. N. (2017). Dynamic scheduling of aircraft landings. European Journal of Operational Research, 258(1), 315–327. https://doi.org/10.1016/j.ejor.2016.08.015CrossRef

Biesinger, F., Meike, D., Kraß, B., & Weyrich, M. (2019). A digital twin for production planning based on cyber-physical systems: A case study for a cyber-physical system-based creation of a digital twin. Procedia CIRP, 79, 355–360. https://doi.org/10.1016/j.procir.2019.02.087CrossRef

Blackstone, J. H., Phillips, D. T., & Hogg, G. L. (1982). A state-of-the-art survey of dispatching rules for manufacturing job shop operations. International Journal of Production Research, 20(1), 27–45. https://doi.org/10.1080/00207548208947745CrossRef

Brah, S. A. (1996). A comparative analysis of due date based job sequencing rules in a flow shop with multiple processors. Production Planning and Control, 7(4), 362–373. https://doi.org/10.1080/09537289608930364CrossRef

Brah, S. A., & Hunsucker, J. L. (1991). Branch and bound algorithm for the flow shop with multiple processors. European Journal of Operational Research, 51(1), 88–99. https://doi.org/10.1016/0377-2217(91)90148-OCrossRef

Chansombat, S., Pongcharoen, P., & Hicks, C. (2019). A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry. International Journal of Production Research, 57(1), 61–82. https://doi.org/10.1080/00207543.2018.1459923CrossRef

Chinesta, F., Cueto, E., Abisset-Chavanne, E., Duval, J. L., & Khaldi, F. E. (2020). Virtual, digital and hybrid twins: A new paradigm in data-based engineering and engineered data. Archives of Computational Methods in Engineering, 27(1), 105–134. https://doi.org/10.1007/s11831-018-9301-4CrossRef

Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230–244. https://doi.org/10.1016/S0377-2217(01)00355-1CrossRef

Dagli, C., & Huggahalli, R. (1991). A neural network architecture for faster dynamic scheduling in manufacturing systems. In 1991 IEEE International Conference on Robotics and Automation Proceedings (pp. 2408–2413 vol.3). Presented at the 1991 IEEE International Conference on Robotics and Automation Proceedings. https://doi.org/10.1109/ROBOT.1991.131983

De Ginste, L. V., Goos, J., Schamp, M., Claeys, A., Hoedt, S., Bauters, K., et al. (2019). Defining flexibility of assembly workstations through the underlying dimensions and impacting drivers. Procedia Manufacturing, 39, 974–982. https://doi.org/10.1016/j.promfg.2020.01.391CrossRef

Fuchigami, H. Y., & Rangel, S. (2018). A survey of case studies in production scheduling: Analysis and perspectives. Journal of Computational Science, 25, 425–436. https://doi.org/10.1016/j.jocs.2017.06.004CrossRef

Fuller, A., Fan, Z., Day, C., & Barlow, C. (2020). Digital twin: enabling technologies, challenges and open research. IEEE Access, 8, 108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358CrossRef

Gavrilas, M. (2010). Heuristic and metaheuristic optimization techniques with application to power systems. In Proceedings of the 12th WSEAS international conference on Mathematical methods and computational techniques in electrical engineering, Romania.

Gearhart, J. L., Adair, K. L., Durfee, J. David., Jones, K. A., Martin, N., & Detry, R. J. (2013). Comparison of open-source linear programming solvers. vol. SAND2013-8847, pp. 1104761, https://doi.org/10.2172/1104761

Goli, A., Tirkolaee, E. B., & Soltani, M. (2019). A robust just-in-time flow shop scheduling problem with outsourcing option on subcontractors. Production and Manufacturing Research, 7(1), 294–315. https://doi.org/10.1080/21693277.2019.1620651CrossRef

Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. H. G. R. (1979). Optimization and approximation in deterministic sequencing and scheduling: a survey. In Annals of Discrete Mathematics, 5, 287–326. https://doi.org/10.1016/S0167-5060(08)70356-XCrossRef

Graves, S. C. (1981). A review of production scheduling. Operations Research. https://doi.org/10.1287/opre.29.4.646CrossRef

Haupt, R. (1989). A survey of priority rule-based scheduling. Or Spektrum, 11(1), 3–16. https://doi.org/10.1007/BF01721162CrossRef

Hu, S. J. (2013). Evolving paradigms of manufacturing: From mass production to mass customization and personalization. Procedia CIRP, 7, 3–8. https://doi.org/10.1016/j.procir.2013.05.002CrossRef

Janiak, A., Kozan, E., Lichtenstein, M., & Oğuz, C. (2007a). Metaheuristic approaches to the hybrid flow shop scheduling problem with a cost-related criterion. Scheduling in Batch-Processing Industries and Supply Chains, 105(2), 407–424. https://doi.org/10.1016/j.ijpe.2004.05.027CrossRef

Janiak, A., Kozan, E., Lichtenstein, M., & Oğuz, C. (2007b). Metaheuristic approaches to the hybrid flow shop scheduling problem with a cost-related criterion. International Journal of Production Economics, 105(2), 407–424. https://doi.org/10.1016/j.ijpe.2004.05.027CrossRef

Ji, W., & Wang, L. (2017). Big data analytics based fault prediction for shop floor scheduling. Journal of Manufacturing Systems, 43, 187–194. https://doi.org/10.1016/j.jmsy.2017.03.008CrossRef

Kadipasaoglu, S. N., Xiang, W., & Khumawala, B. M. (1997). A comparison of sequencing rules in static and dynamic hybrid flow systems. International Journal of Production Research, 35(5), 1359–1384. https://doi.org/10.1080/002075497195371CrossRef

Kis, T., & Pesch, E. (2005). A review of exact solution methods for the non-preemptive multiprocessor flowshop problem. European Journal of Operational Research, 164(3), 592–608. https://doi.org/10.1016/j.ejor.2003.12.026CrossRef

Kulkarni, K., & Venkateswaran, J. (2014). Iterative Simulation and Optimization approach for job shop scheduling. In Proceedings of the Winter Simulation Conference 2014 (pp. 1620–1631). Presented at the Proceedings of the Winter Simulation Conference 2014. https://doi.org/10.1109/WSC.2014.7020013

Kuo, Y., Yang, T., Cho, C., & Tseng, Y.-C. (2008). Using simulation and multi-criteria methods to provide robust solutions to dispatching problems in a flow shop with multiple processors. Mathematics and Computers in Simulation, 78(1), 40–56. https://doi.org/10.1016/j.matcom.2007.06.002CrossRef

Liu, J., & MacCarthy, B. L. (1996). The classification of FMS scheduling problems. International Journal of Production Research, 34(3), 647–656. https://doi.org/10.1080/00207549608904925CrossRef

Maravelias, C. T. (2018). Chemical production scheduling. Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. https://doi.org/10.1016/B978-0-12-409547-2.14341-0CrossRef

Meng, L., Zhang, C., Shao, X., Zhang, B., Ren, Y., & Lin, W. (2020). More MILP models for hybrid flow shop scheduling problem and its extended problems. International Journal of Production Research, 58(13), 3905–3930. https://doi.org/10.1080/00207543.2019.1636324CrossRef

Miclo, R., Lauras, M., Fontanili, F., Lamothe, J., & Melnyk, S. A. (2019). Demand Driven MRP: Assessment of a new approach to materials management. International Journal of Production Research, 57(1), 166–181. https://doi.org/10.1080/00207543.2018.1464230CrossRef

Mohan, J., Lanka, K., & Rao, A. N. (2019). A review of dynamic job shop scheduling techniques. Procedia Manufacturing, 30, 34–39. https://doi.org/10.1016/j.promfg.2019.02.006CrossRef

Muravev, D., Hu, H., Rakhmangulov, A., & Mishkurov, P. (2021). Multi-agent optimization of the intermodal terminal main parameters by using AnyLogic simulation platform: Case study on the Ningbo-Zhoushan Port. International Journal of Information Management, 57, 102133. https://doi.org/10.1016/j.ijinfomgt.2020.102133CrossRef

Naderi, B., Gohari, S., & Yazdani, M. (2014). Hybrid flexible flowshop problems: Models and solution methods. Applied Mathematical Modelling, 38(24), 5767–5780. https://doi.org/10.1016/j.apm.2014.04.012CrossRef

Negri, E., Ardakani, H. D., Cattaneo, L., Singh, J., Macchi, M., & Lee, J. (2019). A digital twin-based scheduling framework including equipment health index and genetic algorithms. IFAC-PapersOnLine, 52(10), 43–48. https://doi.org/10.1016/j.ifacol.2019.10.024CrossRef

Negri, E., Fumagalli, L., & Macchi, M. (2017). A review of the roles of digital twin in CPS-based production systems. Procedia Manufacturing, 11, 939–948.CrossRef

Omar, M. K., S. C., T., & Suppiah, Y. (2010). Mixed integer programming formulation for hybrid flow shop scheduling problem. In 2010 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 385–389). Presented at the EM), Macao, China: IEEE. https://doi.org/10.1109/IEEM.2010.5674608

Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of Scheduling, 12(4), 417–431. https://doi.org/10.1007/s10951-008-0090-8CrossRef

Pinedo, M. L. (2016). Scheduling. Springer International Publishing. https://doi.org/10.1007/978-3-319-26580-3CrossRef

Portougal, V., & Robb, D. J. (2000). Production scheduling theory: Just where is it applicable? Interfaces, 30(6), 64–76. https://doi.org/10.1287/inte.30.6.64.11623CrossRef

Priore, P., Gómez, A., Pino, R., & Rosillo, R. (2014). Dynamic scheduling of manufacturing systems using machine learning: An updated review. AI EDAM, 28(1), 83–97. https://doi.org/10.1017/S0890060413000516CrossRef

Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: A statistical review of the literature, 1952–1994. IEEE Transactions on Engineering Management, 44(3), 316–329. https://doi.org/10.1109/17.618173CrossRef

Ribas, I., Leisten, R., & Framiñan, J. M. (2010). Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective. Computers and Operations Research, 37(8), 1439–1454. https://doi.org/10.1016/j.cor.2009.11.001CrossRef

Rosen, R., von Wichert, G., Lo, G., & Bettenhausen, K. D. (2015). About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine, 48(3), 567–572. https://doi.org/10.1016/j.ifacol.2015.06.141CrossRef

Rossit, D. A., Tohmé, F., & Frutos, M. (2019). A data-driven scheduling approach to smart manufacturing. Journal of Industrial Information Integration, 15, 69–79. https://doi.org/10.1016/j.jii.2019.04.003CrossRef

Ruiz, R., Şerifoğlu, F. S., & Urlings, T. (2008). Modeling realistic hybrid flexible flowshop scheduling problems. Computers and Operations Research, 35(4), 1151–1175. https://doi.org/10.1016/j.cor.2006.07.014CrossRef

Ruiz, R., & Vázquez-Rodríguez, J. A. (2010). The hybrid flow shop scheduling problem. European Journal of Operational Research, 205(1), 1–18. https://doi.org/10.1016/j.ejor.2009.09.024CrossRef

Salvador, M. S. (1973). A solution to a special class of flow shop scheduling problems. In S. E. Elmaghraby (Ed.), Symposium on the theory of scheduling and its applications (pp. 83–91). Springer.CrossRef

Shafaei, R., & Brunn, P. (1999). Workshop scheduling using practical (inaccurate) data Part 2: An investigation of the robustness of scheduling rules in a dynamic and stochastic environment. International Journal of Production Research, 37(18), 4105–4117. https://doi.org/10.1080/002075499189682CrossRef

Shi, L., Guo, G., & Song, X. (2021). Multi-agent based dynamic scheduling optimisation of the sustainable hybrid flow shop in a ubiquitous environment. International Journal of Production Research, 59(2), 576–597. https://doi.org/10.1080/00207543.2019.1699671CrossRef

Suresh, V., & Chaudhuri, D. (1993). Dynamic scheduling—A survey of research. International Journal of Production Economics, 32(1), 53–63. https://doi.org/10.1016/0925-5273(93)90007-8CrossRef

Tang, D., Dai, M., Salido, M. A., & Giret, A. (2016). Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 81, 82–95. https://doi.org/10.1016/j.compind.2015.10.001CrossRef

Tliba, K., Penas, O., Diallo, T. M. L., Ben Khalifa, R., Ben Yahia, N., & Choley, J.-Y. (2020). Model Based Systems Engineering approach for the improvement of manufacturing system flexibility. In 2020 21st International Conference on Research and Education in Mechatronics (REM) (pp. 1–6). Presented at the 2020 21st International Conference on Research and Education in Mechatronics (REM). https://doi.org/10.1109/REM49740.2020.9313871

Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0—A glimpse. Procedia Manufacturing, 20, 233–238. https://doi.org/10.1016/j.promfg.2018.02.034CrossRef

Vieira, G. E., Herrmann, J. W., & Lin, E. (2003). Rescheduling manufacturing systems: a framework of strategies, policies, and methods. Journal of Scheduling, 6(1), 39–62. https://doi.org/10.1023/A:1022235519958CrossRef

Vignier, A., Billaut, J.-C., & Proust, C. (1999). Les problèmes d’ordonnancement de type « flow-shop » hybride : état de l’art. RAIRO - Operations Research - Recherche Opérationnelle, 33(2), 117–183.CrossRef

Waschneck, B., Altenmüller, T., Bauernhansl, T., & Kyek, A. (2016). Production Scheduling in Complex Job Shops from an Industry 4.0 Perspective: A Review and Challenges in the Semiconductor Industry. In SAMI@ iKNOW (pp. 1–12).

Wei, Z., Song, X., & Wang, D. (2017). Manufacturing flexibility, business model design, and firm performance. International Journal of Production Economics, 193, 87–97. https://doi.org/10.1016/j.ijpe.2017.07.004CrossRef

Xiong, F., Chu, M., Li, Z., Du, Y., & Wang, L. (2021). Just-in-time scheduling for a distributed concrete precast flow shop system. Computers and Operations Research, 129, 105204. https://doi.org/10.1016/j.cor.2020.105204CrossRef

Yang, W., & Takakuwa, S. (2017). Simulation-based dynamic shop floor scheduling for a flexible manufacturing system in the industry 4.0 environment. In 2017 Winter Simulation Conference (WSC) (pp. 3908–3916). IEEE.

Zhang, B., Pan, Q.-K., Gao, L., Meng, L.-L., Li, X.-Y., & Peng, K.-K. (2020). A three-stage multiobjective approach based on decomposition for an energy-efficient hybrid flow shop scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 4984–4999. https://doi.org/10.1109/TSMC.2019.2916088CrossRef

Zhang, H., Ma, L., Sun, J., Lin, H., & Thürer, M. (2019). Digital twin in services and industrial product service systems: review and analysis. Procedia CIRP, 83, 57–60. https://doi.org/10.1016/j.procir.2019.02.131CrossRef

Zhang, M., Tao, F., & Nee, A. Y. C. (2021). Digital twin enhanced dynamic job-shop scheduling. Journal of Manufacturing Systems, 58, 146–156. https://doi.org/10.1016/j.jmsy.2020.04.008CrossRef

Zhao, H., Tang, L., Zhang, Y., & Wang, W. (2008). Simulation and analysis on dynamic job shop scheduling toward networked manufacturing. Journal of System Simulation, 11, 057.

Zhou, L., Zhang, L., Ren, L., & Wang, J. (2019). Real-time scheduling of cloud manufacturing services based on dynamic data-driven simulation. IEEE Transactions on Industrial Informatics, 15(9), 5042–5051. https://doi.org/10.1109/TII.2019.2894111CrossRef

Titel: Digital twin-driven dynamic scheduling of a hybrid flow shop
verfasst von: Khalil Tliba
Thierno M. L. Diallo
Olivia Penas
Romdhane Ben Khalifa
Noureddine Ben Yahia
Jean-Yves Choley
Publikationsdatum: 15.03.2022
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 5/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-022-01922-3

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