Advertisement

A Proactive Model for Joint Maintenance and Logistics Optimization in the Frame of Industrial Internet of Things

  • Alexandros BousdekisEmail author
  • Gregoris Mentzas
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Equipment failures in manufacturing processes concern industries because they can lead to severe issues regarding human safety, environmental impact, reliability, and production costs. The stochastic nature of equipment degradation and the uncertainty about future breakdowns affect significantly the maintenance and inventory decisions. Proactive event processing can facilitate this decision-making process in an Industrial Internet of Things (IIoT) environment, but real-time data processing poses several challenges in efficiency and scalability of the associated information systems. Therefore, appropriate real-time, event-driven algorithms and models are required for deciding on the basis of predictions, ahead of time. We propose a proactive event-driven model for joint maintenance and logistics optimization in a sensor-based, data-rich industrial environment. The proposed model is able to be embedded in a real-time, event-driven information system in order to be triggered by prediction events about the future equipment health state. Moreover, the proposed model handles multiple alternative (imperfect and perfect) maintenance actions and associated spare parts orders and facilitates proactive decision making in the context of Condition-Based Maintenance (CBM). The proposed proactive decision model was validated in real industrial environment and was further evaluated with a comparative and a sensitivity analysis.

Keywords

Proactivity Event processing IIoT Condition-Based Maintenance Spare parts ordering Decision making Predictive maintenance 

Notes

Acknowledgments

This work is partly funded by the European Commission projects: FP7 ProaSense—“The Proactive Sensing Enterprise” (612329) and H2020 UPTIME “Unified Predictive Maintenance System” (768634).

References

  1. Basten, R. J., Van der Heijden, M. C., Schutten, J. M. J., & Kutanoglu, E. (2015). An approximate approach for the joint problem of level of repair analysis and spare parts stocking. Annals of Operations Research, 224(1), 121–145.CrossRefGoogle Scholar
  2. Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of things for enterprise systems of modern manufacturing. IEEE Transactions on Industrial Informatics, 10(2), 1537–1546.CrossRefGoogle Scholar
  3. Bohlin, M., & Wärja, M. (2015). Maintenance optimization with duration-dependent costs. Annals of Operations Research, 224(1), 1–23.CrossRefGoogle Scholar
  4. Bousdekis, A., Magoutas, B., Apostolou, D., & Mentzas, G. (2015a). Review, analysis and synthesis of prognostic-based decision support methods for condition based maintenance. Journal of Intelligent Manufacturing, 29(6), 1–14.CrossRefGoogle Scholar
  5. Bousdekis, A., Papageorgiou, N., Magoutas, B., Apostolou, D., & Mentzas, G. (2015b). A real-time architecture for proactive decision making in manufacturing enterprises. In OTM confederated international conferences “on the move to meaningful internet systems” (pp. 137–146). New York: Springer International Publishing.Google Scholar
  6. Brent, R. P. (1971). An algorithm with guaranteed convergence for finding a zero of a function. The Computer Journal, 14(4), 422–425.CrossRefGoogle Scholar
  7. Elwany, A. H., & Gebraeel, N. Z. (2008). Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions, 40(7), 629–639.CrossRefGoogle Scholar
  8. Engel, Y., Etzion, O., & Feldman, Z. (2012). A basic model for proactive event-driven computing. In: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems (pp. 107–118). ACM.Google Scholar
  9. Etzion, O., & Niblett, P. (2010). Event processing in action. New York: Manning Publications Co.Google Scholar
  10. Feldman, Z., Fournier, F., Franklin, R., & Metzger, A. (2013). Proactive event processing in action: A case study on the proactive management of transport processes (industry article). In: Proceedings of the 7th ACM International Conference on Distributed Event-Based Systems (pp. 97–106). ACM.Google Scholar
  11. Gegenfurtner, K. R. (1992). PRAXIS: Brent’s algorithm for function minimization. Behavior Research Methods, Instruments, & Computers, 24(4), 560–564.CrossRefGoogle Scholar
  12. Guillén, A. J., Crespo, A., Gómez, J. F., & Sanz, M. D. (2016). A framework for effective management of condition based maintenance programs in the context of industrial development of E-maintenance strategies. Computers in Industry, 82, 170–185.CrossRefGoogle Scholar
  13. Hu, R., Yue, C., & Xie, J. (2008). Joint optimization of age replacement and spare ordering policy based on generic algorithm. In: Proceedings of 2008 International Conference on Computational Intelligence And Security (pp. 156–161).Google Scholar
  14. Kapur, K. C., & Pecht, M. (2014). Reliability engineering. Hoboken: John Wiley & Sons.CrossRefGoogle Scholar
  15. Keizer, M. C. O., Teunter, R. H., & Veldman, J. (2017). Joint condition-based maintenance and inventory optimization for systems with multiple components. European Journal of Operational Research, 257(1), 209–222.CrossRefGoogle Scholar
  16. Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP, 16, 3–8.CrossRefGoogle Scholar
  17. Lorén, S., & de Maré, J. (2015). Maintenance for reliability—A case study. Annals of Operations Research, 224(1), 111–119.CrossRefGoogle Scholar
  18. Nosoohi, I., & Hejazi, S. R. (2011). A multi-objective approach to simultaneous determination of spare part numbers and preventive replacement times. Applied Mathematical Modelling, 35(3), 1157–1166.CrossRefGoogle Scholar
  19. Muller, A., Suhner, M. C., & Iung, B. (2008). Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system. Reliability Engineering & System Safety, 93(2), 234–253.CrossRefGoogle Scholar
  20. Pistofidis, P., Emmanouilidis, C., Koulamas, C., Karampatzakis, D., & Papathanassiou, N. (2012). A layered e-maintenance architecture powered by smart wireless monitoring components. In: 2012 IEEE International Conference on Industrial Technology (ICIT) (pp. 390–395). IEEE.Google Scholar
  21. Potocnik, M., & Juric, M. B. (2014). Towards complex event aware services as part of SOA. IEEE Transactions on Services Computing, 7(3), 486–500.CrossRefGoogle Scholar
  22. Riemer, D., Kaulfersch, F., Hutmacher, R., & Stojanovic, L. (2015). StreamPipes: Solving the challenge with semantic stream processing pipelines. In: Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems (pp. 330–331). ACM.Google Scholar
  23. Sarker, R., & Haque, A. (2000). Optimization of maintenance and spare provisioning policy using simulation. Applied Mathematical Modelling, 24(10), 751–760.CrossRefGoogle Scholar
  24. Sejdovic, S., Hegenbarth, Y., Ristow, G. H., & Schmidt, R. (2016). Proactive disruption management system: How not to be surprised by upcoming situations. In: Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems (pp. 281–288). ACM.Google Scholar
  25. Stopar, L. (2015). A Multi-Scale methodology for explaining data streams. Conference on Data Mining and Data Warehouses (SiKDD 2015) held at the 18th International Multiconference on Information Society IS-2015. October 5th, 2015, Ljubljana, Slovenia.Google Scholar
  26. Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., & Lennartson, B. (2016). An event-driven manufacturing information system architecture for industry 4.0. International Journal of Production Research, 55(5), 1–15.CrossRefGoogle Scholar
  27. Van Horenbeek, A., Buré, J., Cattrysse, D., Pintelon, L., & Vansteenwegen, P. (2013). Joint maintenance and inventory optimization systems: A review. International Journal of Production Economics, 143(2), 499–508.CrossRefGoogle Scholar
  28. Wang, W. (2012). A stochastic model for joint spare parts inventory and planned maintenance optimization. European Journal of Operational Research, 216(1), 127–139.CrossRefGoogle Scholar
  29. Wang, W., Pecht, M. G., & Liu, Y. (2012). Cost optimization for canary-equipped electronic systems in terms of inventory control and maintenance decisions. IEEE Transactions on Reliability, 61(2), 466–478.CrossRefGoogle Scholar
  30. Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.Google Scholar
  31. Wu, S. J., Gebraeel, N., Lawley, M. A., & Yih, Y. (2007). A neural network integrated decision support system for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 37(2), 226–236.CrossRefGoogle Scholar
  32. Xie, J., & Wang, H. (2008). Joint optimization of condition-based preventive maintenance and spare ordering policy. In: Proceedings of 4th international conference on wireless communications networking and mobile computing, (WiCOM 08) (pp. 1–5).Google Scholar
  33. Zimmermann, A., Schmidt, R., Sandkuhl, K., Wißotzki, M., Jugel, D., & Möhring, M. (2015, September). Digital enterprise architecture-transformation for the internet of things. In: Enterprise Distributed Object Computing Workshop (EDOCW), 2015 IEEE 19th International (pp. 130–138). IEEE.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS)National Technical University of Athens (NTUA)AthensGreece

Personalised recommendations