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A Hyper-heuristic for Dynamic Scheduling of Cyber-Physical Production Systems Using Incremental Learning

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Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future (SOHOMA 2023)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1136))

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Abstract

To ensure efficient dynamic manufacturing scheduling within a Cyber-Physical Production System (CPPS), it is essential to develop reactive control architectures. Heuristic-based optimization algorithms can provide this necessary reactivity and agility. In this paper, a hyper-heuristic is proposed. A set of atomic rules for resource selection are combined in a decisional strategy previously developed. The latter results from an optimization-simulation process. A new incremental learning mechanism is introduced in this article; it allows the system to evolve smoothly to integrate new events from the CPPS and its environment. A comparative study with a metaheuristic and heuristics on 56 instances, with family-dependent setup and processing times, demonstrates the interest of the proposed approach.

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Notes

  1. 1.

    https://github.com/wbouazza/OpenData4Manufacturing.

References

  1. Bouazza, W., Sallez, Y., Trentesaux, D.: Dynamic scheduling of manufacturing systems: a product-driven approach using hyper-heuristics. Int. J. Comput. Integr. Manuf. 34, 641–665 (2021). https://doi.org/10.1080/0951192X.2021.1925969

    Article  Google Scholar 

  2. Bouazza, W., Sallez, Y., Trentesaux, D.: Toward efficient fms scheduling through rules combination using an optimization-simulation mechanism. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Joblot, L. (eds.) SOHOMA 2021. SCI, vol. 1034, pp. 559–571. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-99108-1_40

    Chapter  Google Scholar 

  3. Ansari, F., Glawar, R., Nemeth, T.: PriMa: a prescriptive maintenance model for cyber-physical production systems. Int. J. Comput. Integr. Manuf. 32, 482–503 (2019). https://doi.org/10.1080/0951192X.2019.1571236

    Article  Google Scholar 

  4. Liu, C., Jiang, P., Jiang, W.: Web-based digital twin modeling and remote control of cyber-physical production systems. Robot Comput. Integr. Manuf. 64, 101956 (2020). https://doi.org/10.1016/j.rcim.2020.101956

    Article  Google Scholar 

  5. Paredes-Astudillo, Y.A., Moreno, D., Vargas, A.-M., et al.: Human fatigue aware cyber-physical production system. In: 2020 IEEE International Conference on Human-Machine Systems (ICHMS), pp. 1–6. IEEE (2020)

    Google Scholar 

  6. Cardin, O.: Classification of cyber-physical production systems applications: proposition of an analysis framework. Comput. Ind. 104, 11–21 (2019). https://doi.org/10.1016/j.compind.2018.10.002

    Article  Google Scholar 

  7. Cardin, O., Trentesaux, D.: General concepts. In: Digitalization and Control of Industrial Cyber‐Physical Systems, pp 1–16. Wiley (2022)

    Google Scholar 

  8. Capawa Fotsoh, E., Mebarki, N., Castagna, P., Berruet, P.: A classification for reconfigurable manufacturing systems. In: Benyoucef, L. (ed.) Reconfigurable Manufacturing Systems: From Design to Implementation. Springer Series in Advanced Manufacturing. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-28782-5_2

    Chapter  Google Scholar 

  9. Framinan, J.M., Leisten, R., Ruiz García, R.: Manufacturing Scheduling Systems. Springer, London, London (2014). https://doi.org/10.1007/978-1-4471-6272-8

    Book  Google Scholar 

  10. Corning, P.A.: The re-emergence of “emergence”: a venerable concept in search of a theory. Complexity 7, 18–30 (2002). https://doi.org/10.1002/cplx.10043

    Article  MathSciNet  Google Scholar 

  11. Anuradha, V.P., Sumathi, D.: A survey on resource allocation strategies in cloud computing. In: International Conference on Information Communication and Embedded Systems (ICICES2014), pp. 1–7. IEEE (2014)

    Google Scholar 

  12. Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20, 110–124 (2016). https://doi.org/10.1109/TEVC.2015.2429314

    Article  Google Scholar 

  13. Allahverdi, A.: The third comprehensive survey on scheduling problems with setup times/costs. Eur. J. Oper. Res. 246, 345–378 (2015). https://doi.org/10.1016/j.ejor.2015.04.004

    Article  MathSciNet  Google Scholar 

  14. Henderson, D., Jacobson, S.H., Johnson, A.W.: The Theory and Practice of Simulated Annealing. In: Handbook of Metaheuristics, pp. 287–319. Kluwer Academic Publishers, Boston (2006)

    Google Scholar 

  15. Jorapur, V.S., Puranik, V.S., Deshpande, A.S., Sharma, M.: A promising initial population based genetic algorithm for job shop scheduling problem. J. Softw. Eng. Appl. 09, 208–214 (2016). https://doi.org/10.4236/jsea.2016.95017

    Article  Google Scholar 

  16. Liou, C.D., Hsieh, Y.C.: A hybrid algorithm for the multi-stage flow shop group scheduling with sequence-dependent setup and transportation times. Int. J. Prod. Econ. 170, 258–267 (2015). https://doi.org/10.1016/j.ijpe.2015.10.002

    Article  Google Scholar 

  17. Burke, E.K., Hyde, M.R., Kendall, G., et al.: A classification of hyper-heuristic approaches: revisited. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, 2nd edn., vol. 272, pp. 453–477. Springer, New York (2019). https://doi.org/10.1007/978-3-319-91086-4_14

  18. Burke, E., Hyde, M., Kendall, G.: A classification of hyper-heuristic approaches. In: Gendreau, M., Potvin, J.Y. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146, pp. 449–468. Springer, Boston (2010). https://doi.org/10.1007/978-1-4419-1665-5_15

    Chapter  Google Scholar 

  19. Vázquez Rodríguez, J.A., Petrovic, S., Salhi, A.: A combined meta-heuristic with hyper-heuristic approach to the scheduling of the hybrid flow shop with sequence dependent setup times and uniform machines. In: Proceedings of the 3rd Multidisciplinary International Conference on Scheduling: Theory and Applications, pp. 506–513 (2007)

    Google Scholar 

  20. Mascia, F., López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T.: From grammars to parameters: automatic iterated greedy design for the permutation flow-shop problem with weighted tardiness. In: Nicosia, G., Pardalos, P. (eds.) LION 2013. LNCS (LNAI and LNB), vol. 7997, pp. 321–334. Springer, Cham (2013). https://doi.org/10.1007/978-3-642-44973-4_36

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, 44000, Nantes, France

    Wassim Bouazza & Olivier Cardin

  2. LAMIH UMRCNRS 8201, Université Polytechnique Hauts-de-France, Valenciennes, France

    Yves Sallez

Authors
  1. Wassim Bouazza
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  2. Yves Sallez
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  3. Olivier Cardin
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Corresponding author

Correspondence to Wassim Bouazza .

Editor information

Editors and Affiliations

  1. Faculty of Automatic Control and Computer Science (Automatica), University Politehnica of Bucharest, Bucharest, Romania

    Theodor Borangiu

  2. UPHF, LAMIH UMR CNRS 8201, Université Polytechnique Hauts de France, Valenciennes cedex 9, France

    Damien Trentesaux

  3. Polytechnic Institute of Bragança, Braganca, Portugal

    Paulo Leitão

  4. Polytech Annecy-Chambéry, LISTIC, Annecy, France

    Lamia Berrah

  5. SYMME, Polytech Annecy-Chambéry, Annecy, France

    Jose-Fernando Jimenez

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Bouazza, W., Sallez, Y., Cardin, O. (2024). A Hyper-heuristic for Dynamic Scheduling of Cyber-Physical Production Systems Using Incremental Learning. In: Borangiu, T., Trentesaux, D., Leitão, P., Berrah, L., Jimenez, JF. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2023. Studies in Computational Intelligence, vol 1136. Springer, Cham. https://doi.org/10.1007/978-3-031-53445-4_17

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