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.
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References
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.
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.
Bohlin, M., & Wärja, M. (2015). Maintenance optimization with duration-dependent costs. Annals of Operations Research, 224(1), 1–23.
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.
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.
Brent, R. P. (1971). An algorithm with guaranteed convergence for finding a zero of a function. The Computer Journal, 14(4), 422–425.
Elwany, A. H., & Gebraeel, N. Z. (2008). Sensor-driven prognostic models for equipment replacement and spare parts inventory. IIE Transactions, 40(7), 629–639.
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.
Etzion, O., & Niblett, P. (2010). Event processing in action. New York: Manning Publications Co.
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.
Gegenfurtner, K. R. (1992). PRAXIS: Brent’s algorithm for function minimization. Behavior Research Methods, Instruments, & Computers, 24(4), 560–564.
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.
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).
Kapur, K. C., & Pecht, M. (2014). Reliability engineering. Hoboken: John Wiley & Sons.
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.
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.
Lorén, S., & de Maré, J. (2015). Maintenance for reliability—A case study. Annals of Operations Research, 224(1), 111–119.
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.
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.
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.
Potocnik, M., & Juric, M. B. (2014). Towards complex event aware services as part of SOA. IEEE Transactions on Services Computing, 7(3), 486–500.
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.
Sarker, R., & Haque, A. (2000). Optimization of maintenance and spare provisioning policy using simulation. Applied Mathematical Modelling, 24(10), 751–760.
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.
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.
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.
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.
Wang, W. (2012). A stochastic model for joint spare parts inventory and planned maintenance optimization. European Journal of Operational Research, 216(1), 127–139.
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.
Watkins, C. J., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292.
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.
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).
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.
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).
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Bousdekis, A., Mentzas, G. (2019). A Proactive Model for Joint Maintenance and Logistics Optimization in the Frame of Industrial Internet of Things. In: Sifaleras, A., Petridis, K. (eds) Operational Research in the Digital Era – ICT Challenges. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-95666-4_3
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DOI: https://doi.org/10.1007/978-3-319-95666-4_3
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