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Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration

Authors Willem van Jaarsveld , Alp Akçay , Laurens Bliek , Paulo da Costa , Mathijs de Weerdt , Rik Eshuis , Stella Kapodistria , Uzay Kaymak , Verus Pronk, Geert-Jan van Houtum , Peter Verleijsdonk , Sicco Verwer , Simon Voorberg , Yingqian Zhang

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Author Details

Willem van Jaarsveld
  • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of, Technology, The Netherlands
Alp Akçay
  • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of, Technology, The Netherlands
Laurens Bliek
  • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of, Technology, The Netherlands
Paulo da Costa
  • Etsy Inc., Dublin, Ireland
Mathijs de Weerdt
  • Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of , Technology, The Netherlands
Rik Eshuis
  • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of , Technology, The Netherlands
Stella Kapodistria
  • Department of Mathematics and Computer, Science, Eindhoven University of Technology, The Netherlands
Uzay Kaymak
  • Jheronimus Academy of Data Science, Eindhoven University of Technology, The Netherlands
Verus Pronk
  • Philips Research Laboratories, Eindhoven, The Netherlands
Geert-Jan van Houtum
  • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of, Technology, The Netherlands
Peter Verleijsdonk
  • Department of Mathematics and Computer, Science, Eindhoven University of Technology, The Netherlands
Sicco Verwer
  • Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of , Technology, The Netherlands
Simon Voorberg
  • Department of Information Systems Supply Chain Management and Decision Analysis, NEOMA Business School, Rouen, France
Yingqian Zhang
  • Department of Industrial Engineering and Innovation Sciences, Eindhoven University of , Technology, The Netherlands

Funding

This work was supported by the "Netherlands Organisation for Scientific Research" (NWO). Project: NWO Big data - Real Time ICT for Logistics. Number: 628.009.012.

Acknowledgements

We want to thank all the involved private partners who have contributed to this project.

Cite AsGet BibTex

Willem van Jaarsveld, Alp Akçay, Laurens Bliek, Paulo da Costa, Mathijs de Weerdt, Rik Eshuis, Stella Kapodistria, Uzay Kaymak, Verus Pronk, Geert-Jan van Houtum, Peter Verleijsdonk, Sicco Verwer, Simon Voorberg, and Yingqian Zhang. Real-Time Data-Driven Maintenance Logistics: A Public-Private Collaboration. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 5:1-5:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/OASIcs.Commit2Data.5

Abstract

The project "Real-time data-driven maintenance logistics" was initiated with the purpose of bringing innovations in data-driven decision making to maintenance logistics, by bringing problem owners in the form of three innovative companies together with researchers at two leading knowledge institutions. This paper reviews innovations in three related areas: How the innovations were inspired by practice, how they materialized, and how the results impact practice.

Subject Classification

ACM Subject Classification
  • General and reference → Surveys and overviews
Keywords
  • Data
  • Maintenance
  • Logistics
  • Optimization
  • Research
  • Project

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References

  1. Alp Akcay. An alert-assisted inspection policy for a production process with imperfect condition signals. European Journal of Operational Research, 298(2):510-525, 2022. URL: https://doi.org/10.1016/J.EJOR.2021.05.051.
  2. James Bergstra, Daniel Yamins, and David Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In Sanjoy Dasgupta and David McAllester, editors, Proceedings of the 30th International Conference on Machine Learning, volume 28 of Proceedings of Machine Learning Research, pages 115-123. PMLR, 17-19 June 2013. URL: http://proceedings.mlr.press/v28/bergstra13.html.
  3. L Bliek, P da Costa, R Refaei Afshar, Y Zhang, T Catshoek, D Vos, S Verwer, and F Schmitt-Ulms. The first AI4TSP competition. URL: https://paulorocosta.gitbook.io/ai4tsp-competition/.
  4. L. Bliek, H. R. G. W. Verstraete, M. Verhaegen, and S. Wahls. Online optimization with costly and noisy measurements using random Fourier expansions. IEEE Transactions on Neural Networks and Learning Systems, 29(1):167-182, 2018. URL: https://doi.org/10.1109/TNNLS.2016.2615134.
  5. Laurens Bliek, Arthur Guijt, Rickard Karlsson, Sicco Verwer, and Mathijs De Weerdt. EXPObench: Benchmarking surrogate-based optimisation algorithms on expensive black-box functions. arXiv preprint arXiv:2106.04618, 2021. URL: https://arxiv.org/abs/2106.04618.
  6. Laurens Bliek, Arthur Guijt, Sicco Verwer, and Mathijs De Weerdt. Black-box mixed-variable optimisation using a surrogate model that satisfies integer constraints. In Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO '21, pages 1851-1859, New York, NY, USA, 2021. Association for Computing Machinery. URL: https://doi.org/10.1145/3449726.3463136.
  7. Laurens Bliek, Arthur Guijt, Sicco Verwer, Mathijs De Weerdt, and Rickard Karlsson. Raw data of the EXPensive Optimization benchmark library (EXPObench). Available at https://doi.org/10.4121/14247179.v2, 2021.
  8. Laurens Bliek, Sicco Verwer, and Mathijs De Weerdt. Black-box combinatorial optimization using models with integer-valued minima. Annals of Mathematics and Artificial Intelligence, 89:639-653, 2021. URL: https://doi.org/10.1007/s10472-020-09712-4.
  9. Paulo da Costa, Jason Rhuggenaath, Yingqian Zhang, and Alp Akcay. Learning 2-opt heuristics for the traveling salesman problem via deep reinforcement learning. In Asian Conference on Machine Learning, pages 465-480. PMLR, 2020. Google Scholar
  10. Paulo da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay, and Uzay Kaymak. Learning 2-opt heuristics for routing problems via deep reinforcement learning. SN Computer Science, 2:1-16, 2021. Google Scholar
  11. Paulo da Costa, Peter Verleijsdonk, Simon Voorberg, Alp Akcay, Stella Kapodistria, Willem Van Jaarsveld, and Yingqian Zhang. Policies for the dynamic traveling maintainer problem with alerts. European Journal of Operational Research, 305(3):1141-1152, 2023. URL: https://doi.org/10.1016/J.EJOR.2022.06.044.
  12. Paulo Roberto De Oliveira da Costa, Alp Akcay, Yingqian Zhang, and Uzay Kaymak. Attention and long short-term memory network for remaining useful lifetime predictions of turbofan engine degradation. International Journal of Prognostics and Health Management, 10(4), 2019. Google Scholar
  13. Paulo Roberto de Oliveira da Costa, Alp Akçay, Yingqian Zhang, and Uzay Kaymak. Remaining useful lifetime prediction via deep domain adaptation. Reliability Engineering & System Safety, 195:106682, 2020. URL: https://doi.org/10.1016/J.RESS.2019.106682.
  14. Paulo De Oliveira Da Costa et al. Real-time data-driven maintenance logistics. 3rd Place Commit2Data poster competition at ICT.OPEN, March 2019. Google Scholar
  15. Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In International conference on learning and intelligent optimization, pages 507-523. Springer, 2011. URL: https://doi.org/10.1007/978-3-642-25566-3_40.
  16. R. Martens. Controlling TUSP features during instance generation via Bayesian optimization. Eindhoven University of Technology, Master’s thesis, 2022. Google Scholar
  17. Joaquim R. R. A. Martins and Andrew Ning. Engineering Design Optimization. Cambridge University Press, 2021. URL: https://doi.org/10.1017/9781108980647.
  18. Binxin Ru, Ahsan Alvi, Vu Nguyen, Michael A. Osborne, and Stephen Roberts. Bayesian optimisation over multiple continuous and categorical inputs. In Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 8276-8285. PMLR, 13-18 July 2020. URL: http://proceedings.mlr.press/v119/ru20a.html.
  19. B. Shahriari, Kevin Swersky, Ziyu Wang, R. Adams, and N. D. Freitas. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104(1):148-175, 2016. URL: https://doi.org/10.1109/JPROC.2015.2494218.
  20. L. J. Van den Nieuwelaar. Automatic algorithm configuration with search heuristics for the train unit shunting problem. Eindhoven University of Technology, Master’s thesis, 2021. Google Scholar
  21. L. Van der Knaap. Contextual hyperparameter optimization for the train unit shunting problem. Delft University of Technology, Master’s thesis, 2021. Google Scholar
  22. Peter Verleijsdonk, Willem van Jaarsveld, and Stella Kapodistria. Scalable policies for the dynamic traveling multi-maintainer problem with alerts. Eur. J. Oper. Res., 319:121-134, 2024. URL: https://doi.org/10.1016/J.EJOR.2024.05.049.
  23. S Voorberg, W Van Jaarsveld, R Eshuis, and GJ Van Houtum. Information acquisition for service contract quotations made by repair shops. European Journal of Operational Research, 305(3):1166-1177, 2023. URL: https://doi.org/10.1016/J.EJOR.2022.06.048.
  24. Simon Voorberg, Rik Eshuis, Willem Van Jaarsveld, and GJ Van Houtum. Decisions for information or information for decisions? Optimizing information gathering in decision-intensive processes. Decision Support Systems, 151:113632, 2021. URL: https://doi.org/10.1016/J.DSS.2021.113632.
  25. H.G.P. Wismans. Domain adaptation for prognostics in the aerospace industry. Eindhoven University of Technology, Master’s thesis, 2019. Google Scholar
  26. Dietmar Wolz. FCMAES - a Python-3 derivative-free optimization library. Available at https://github.com/dietmarwo/fast-cma-es, 2022.
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