برنامه‌ریزی توأم نگهداری تعمیرات و تولید مبتنی‌بر شرایط در یک سیستم با افق برنامه‌ریزی محدود و تقاضا از پیش تعیین ‌شده

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری مهندسی صنایع، گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه کردستان، سنندج، ایران

2 استاد گروه مهندسی صنایع، دانشکدۀ مهندسی، دانشگاه کردستان، سنندج، ایران

3 دانشیار گروه مهندسی صنایع، دانشکدۀ مدیریت مهندسی، دانشگاه صنعتی کرمانشاه، کرمانشاه، ایران

10.22084/ier.2025.30085.2183

چکیده

هدف مدیریت در یک سیستم تولیدی کاهش هزینه‌های کلی نگهداری تعمیرات و افزایش دسترس‌پذیری و یا قابلیت اطمینان سیستم است. در دهه‌های گذشته به‌علت این‌که هزینه‌های نگهداری و تعمیرات به بخش بزرگی از هزینه‌های چرخه عمر سیستم تبدیل شده است؛ بنابراین این اقدامات پیچیده‌تر و حیاتی‌تر ازقبل تعریف شده و از اهمیت بیشتری برخوردار هستند. نگهداری و تعمیرات مبتنی‌بر شرایط یکی از انواع رایج استراتژی‌های نگهداری و تعمیرات است که اقدامات لازم را براساس پایش وضعیت سیستم پیشنهاد می‌کند. پیشرفت‌های اخیر در حوزه سنسورها و اینترنت اشیا به‌طور فزاینده‌ای امکان نظارت و کنترل تجهیزات تولید را از راه دور و در زمان واقعی فراهم می‌کند. سازمان‌ها می‌توانند از این فرصت‌ها برای کاهش هزینه‌ها و افزایش قابلیت اطمینان بااستفاده از سیاست‌های نگهداری و تعمیرات مبتنی‌بر شرایط‏ استفاده کنند. علاوه‌بر اقدامات نگهداری و تعمیرات گزینه پیشنهادی دیگر به‌جهت کنترل میزان خرابی سیستم، کاهش هزینه‌ها و افزایش قابلیت اطمینان و دسترس‌پذیری سیستم، اتخاذ سیاست‌های تولید مبتنی‌بر شرایط است که خرابی تجهیزات را ازطریق تطبیق پویای نرخ تولید کنترل می‌کند. در این پژوهش یک سیاست نگهداری و تعمیرات و تولید مبتنی‌بر شرایط پویا در سیستمی که دارای تقاضای ازپیش تعیین‌شده می‌باشد، معرفی شده است که هردو سیاست نگهداری تعمیرات و تولید را ادغام می‌کند. ادغام تصمیمات تولید مبتنی‌بر شرایط در یک سیاست نگهداری و تعمیرات مبتنی‌بر شرایط، خطر خرابی سیستم را کاهش داده و باعث می‌شود اقدامات نگهداری و تعمیرات کمتری انجام شود و درنتیجه هزینه‌های کلی سیستم کاهش یابد. این مسأله به‌وسیله فرآیند تصمیم‌گیری مارکوف مدل‌سازی شده و سپس به‌وسیله الگوریتم تکرار ارزش، سیاست‌های بهینه برای هر حالت اتحاذ گردیده است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Joint Condition-Based Maintenance and Condition-Based Production Optimization in Production System with Finite Planning Horizon and Predetermined Demand

نویسندگان [English]

  • Pourya Mohammadipour 1
  • Hiwa Farughi 2
  • Hasan Rasay 3
1 PhD student in Industrial Engineering, Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
2 Professor Department of Industrial Engineering, Faculty of Engineering, University of Kurdistan, Sanandaj, Iran
3 Associate Professor, Department of Industrial Engineering, Faculty of Engineering Management, Kermanshah University of Technology, Kermanshah, IranTechnology, Kermanshah, Iran
چکیده [English]

The goal of management in a production system is to reduce the overall maintenance costs and increase the availability or reliability of the system. Given that maintenance costs have become a large part of system life cycle costs, these measures are becoming more complex and important. Condition-based maintenance is one of the most common types of maintenance strategies that suggest actions based on monitoring the system's condition. Recent developments in the field of sensors and the Internet of Things increasingly enable remote monitoring and control of production equipment in real time. Organizations can take advantage of these opportunities to reduce costs and increase reliability by using condition-based maintenance policies. In addition to maintenance actions, another suggested option to control system failure rates, reduce costs, and increase system reliability and availability is to adopt condition-based production policies that control equipment failure through dynamic adaptation of production rates. In this paper, condition-based maintenance and production in a system with fixed demand is introduced, which integrates both maintenance and production policies. Integrating production decisions into a condition-based maintenance policy reduces the risk of system failure and causes fewer maintenance actions to be taken, thereby reducing overall system costs. This problem is modeled by the Markov decision process and then by the value iteration algorithm, the optimal policies are adopted for each state.

کلیدواژه‌ها [English]

  • Condition-Based Maintenance
  • Condition-Based Production
  • Condition Monitoring
  • Markov Decision Process
  • Reinforcement Learning
  • Value Iteration Algorithm

مراجع

  • Wireman, T., (2014). Benchmarking Best practices for maintenance, reliability and asset management. Industrial Press. ISBN: 978-0831135034
  • Blanco, M. I., (2009). The economics of wind energy. Renewable and sustainable energy reviews, 13(6-7): p. 1372-1382. https://doi.org/10.1016/j.rser.2008.09.004
  • Röckmann, C., S. Lagerveld, and J. Stavenuiter, (2017). Operation and maintenance costs of offshore wind farms and potential multi-use platforms in the Dutch North Sea. Aquaculture Perspective of Multi-Use Sites in the Open Ocean: The Untapped Potential for Marine Resources in the Anthropocene, p. 97-113. ISBN 978-3-319-51159-7
  • Irena, I., 2018(2018). Renewable power generation costs in 2017. Report. International Renewable Energy Agency, Abu Dhabi, ISBN: 978-92-9260-040-2
  • Feng Y, Qiu Y, Crabtree CJ, Long H, Tavner PJ. (2013 ). Monitoring wind turbine gearboxes. Wind energy. Jul, 16(5):728-40. https://doi.org/10.1002/we.1521
  • Zhang, T., R. Dwight, and K. El-Akruti. (2015). Condition based maintenance and operation of wind turbines. In: Engineering Asset Management-Systems, Professional Practices and Certification: Proceedings of the 8th World Congress on Engineering Asset Management (WCEAM 2013) & the 3rd International Conference on Utility Management & Safety (ICUMAS). Springer. https://doi.org/10.1007/978-3-319-09507-3_87
  • Nourelfath, M. and F. Yalaoui, (2012). Integrated load distribution and production planning in series-parallel multi-state systems with failure rate depending on load. Reliability Engineering & System Safety, 106: p. 138-145. https://doi.org/10.1016/j.ress.2012.06.006
  • Filus, J., (1987). The load optimization of a repairable system with gamma-distributed time-to-failure. Reliability Engineering, 18(4): p. 275-28. https://doi.org/10.1016/0143-8174(87)90032-1
  • Ang, C.-W. and C.-K. (2007). Tham, Analysis and optimization of service availability in a HA cluster with load-dependent machine availability. IEEE Transactions on parallel and distributed systems, 18(9): p. 1307-1319. https://doi.org/1109/TPDS.2007.1071
  • Iyer, R.K. and D.J. (1986). Rossetti, A measurement-based model for workload dependence of CPU errors. IEEE Transactions on computers, 100(6): p. 511-519. https://doi.org/10.1109/TC.1986.5009428
  • Dolinšek, S., B. Šuštaršič, and J. Kopač, (2001). Wear mechanisms of cutting tools in high-speed cutting processes. Wear, 250(1-12): p. 349-356. https://doi.org/10.1016/S0043-1648(01)00620-2
  • Hao L, Liu K, Gebraeel N, Shi J. (2015 ). Controlling the residual life distribution of parallel unit systems through workload adjustment. IEEE Transactions on Automation Science and Engineering. 26;14(2):1042-52. https://doi.org/1109/TASE.2015.2481703
  • Sun Q, Ye ZS, Revie M, Walls L. (2019). Reliability modeling of infrastructure load-sharing systems with workload adjustment. IEEE Transactions on Reliability. 68(4):1283-95. https://doi.org/1109/TR.2019.2900845
  • Keizer MC, Teunter RH, Veldman J, Babai MZ. (2018). Condition-based maintenance for systems with economic dependence and load sharing. International Journal of Production Economics. 195: 319-27. https://doi.org/10.1016/j.ijpe.2017.10.030
  • Truong‐Ba H, Borghesani P, Cholette ME, Ma L. (2020). Optimization of condition‐based maintenance considering partial opportunities. Quality and Reliability Engineering International. 36(2): 529-46. https://doi.org/10.1002/qre.2585
  • فاروقی، هیوا، رسائی، حسن، رمضانی، سمیه. (1402). ارائه یک مدل ریاضی جهت برنامه‌ریزی نگهداری و تعمیرات برمبنای اطلاعات نمودارهای کنترل با رویکرد زنجیره مارکوف، نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید. https://doi.org/22084/ier.2024.5571
  • بابایی مراد، سمانه، فتاحی، پرویز، باقری، حسن. (1398). بهینه‌سازی توأم سیاست زمان‌بندی تولید و نگهداری و تعمیرات با درنظر گرفتن کمبود از نوع پس‌افت و تقاضا به‌صورت احتمالی. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید. https://doi.org/22084/ier.2019.19543.1870
  • Zheng, R. and V. Makis, (2020). Optimal condition-based maintenance with general repair and two dependent failure modes. Computers & Industrial Engineering, 141: p. 106322. https://doi.org/10.1016/j.cie.2020.106322
  • Park, M. and H. Pham, (2023). Condition-based maintenance for a degradation-shock dependence system under warranty. International Journal of Production Research, 61(15): p. 5212-5227. https://doi.org/10.1080/00207543.2022.2099319
  • Ward, T., K. Jenab, and J. Ortega-Moody, (2024). Machine learning models for condition-based maintenance with regular truncated signals. Decision Science Letters, 13(1): 197-210. https://doi.org/10.36001/ijphm.2024.v15i2.3850
  • Bouvard K, Artus S, Bérenguer C, Cocquempot V. (2011). Condition-based dynamic maintenance operations planning & grouping. Application to commercial heavy vehicles. Reliability Engineering & System Safety. 96(6): 601-10. https://doi.org/10.1016/j.ress.2010.11.009
  • de Jonge, B., R. Teunter, and T. Tinga, (2017). The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance. Reliability engineering & system safety, 158: 21-30. https://doi.org/10.1016/j.ress.2016.10.002
  • Kim, M.J. and Makis, (2013). Joint optimization of sampling and control of partially observable failing systems. Operations Research, 61(3): p. 777-790. https://doi.org/10.1287/opre.2013.1171
  • Panagiotidou, S. and G. Tagaras, (2010). Statistical process control and condition‐based maintenance: A meaningful relationship through data sharing. Production and operations management, 19(2): p. 156-171. https://doi.org/10.1111/j.1937-5956.2009.01073.x
  • Mula J, Poler R, García-Sabater JP, Lario FC. (2006). Models for production planning under uncertainty: A review. International journal of production economics. 103(1): 271-85. https://doi.org/10.1016/j.ijpe.2005.09.001
  • Sethi SP, Yan H, Zhang H, Zhang Q. (2002). Optimal and hierarchical controls in dynamic stochastic manufacturing systems: A survey. Manufacturing & Service Operations Management. 4(2): 133-70. https://doi.org/10.1287/msom.4.2.133.281
  • Francie KA, Jean-Pierre K, Pierre D, Victor S, Vladimir P. (2014). Stochastic optimal control of manufacturing systems under production-dependent failure rates. International Journal of Production Economics. 150: 174-87. https://doi.org/10.1016/j.ijpe.2013.12.032
  • Lu, Z., W. Cui, and X. Han, (2015). Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty. Computers & Industrial Engineering, 80: 236-244. https://doi.org/10.1016/j.cie.2014.12.017
  • Hu, J.-Q., P. Vakili, and G.-X. Yu, (1994). Optimality of hedging point policies in the production control of failure prone manufacturing systems. IEEE Transactions on Automatic Control, 39(9): 1875-1880. https://doi.org/1109/9.317116
  • Martinelli, F., (2005). Control of manufacturing systems with a two-value, production-dependent failure rate. Automatica, 41(11): 1943-1948. https://doi.org/10.1016/j.automatica.2005.05.016
  • Martinelli, F., (2007). Optimality of a two-threshold feedback control for a manufacturing system with a production dependent failure rate. IEEE Transactions on Automatic Control, 52(10): p. 1937-1942. https://doi.org/1109/TAC.2007.906229
  • Martinelli, F., (2007). Manufacturing systems with a production dependent failure rate: structure of optimality. IEEE Transactions on Automatic Control, (55(10: 2401-2406. https://doi.org/1109/TAC.2010.2054790
  • رسائی، حسن، فاروقی، هیوا. (1402). بررسی تأثیر مکانیزم‌های مختلف خرابی فرآیندهای تولیدی در مدل‌های یکپارچه نگهداری و تعمیرات و کنترل فرآیند آماری بااستفاده از طراحی آزمایشات. نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید .https://doi.org/22084/ier.2023.27913.2130
  • Zied, H., D. Sofiene, and R. Nidhal, (2011). Optimal integrated maintenance/production policy for randomly failing systems with variable failure rate. International Journal of Production Research, 49(19): 5695-5712. https://doi.org/10.1080/00207543.2010.528063
  • Ayed, S., D. Sofiene, and R. Nidhal, (2012). Joint optimisation of maintenance and production policies considering random demand and variable production rate. International Journal of Production Research, 50(23): 6870-6885. https://doi.org/10.1080/00207543.2011.631601
  • De Jonge, B. and E. Jakobsons, (2018). Optimizing block-based maintenance under random machine usage. European Journal of Operational Research, 265(2): 703-709. https://doi.org/10.1016/j.ejor.2017.07.051
  • Liu B, Wu S, Xie M, Kuo W. (2017). A condition-based maintenance policy for degrading systems with age-and state-dependent operating cost. European Journal of Operational Research. Dec 16;263(3):879-87. https://doi.org/10.1016/j.ejor.2017.05.006
  • Makis, V. and X. Jiang, (2003).Optimal replacement under partial observations. Mathematics of operations research, 28(2): 382-394. https://doi.org/10.1287/moor.28.2.382.14484
  • Sloan, T.W. and J.G. Shanthikumar, (2000). Combined production and maintenance scheduling for a multiple‐product, single‐machine production system. Production and Operations Management, 9(4): p. 379-399. https://doi.org/10.1111/j.1937-5956.2000.tb00465.x
  • Kazaz, B. and T. W. Sloan, (2013). The impact of process deterioration on production and maintenance policies. European Journal of Operational Research, 227(1): p. 88-100. https://doi.org/10.1016/j.ejor.2012.11.052
  • uit het Broek MA, Teunter RH, De Jonge B, Veldman J. (2021). Joint condition-based maintenance and condition-based production optimization. Reliability Engineering & System Safety. 214: 107743. https://doi.org/10.1016/j.ress.2021.107743
  • Broek MA, Teunter RH, De Jonge B, Veldman J. (2021). Joint condition-based maintenance and load-sharing optimization for two-unit systems with economic dependency. European Journal of Operational Research. 295(3): 1119-31. https://doi.org/10.1016/j.ejor.2021.03.044
  • Koopmans, M. and B. de Jonge, (2023). Condition-based maintenance and production speed optimization under limited maintenance capacity. Computers & Industrial Engineering, 179: 109155. https://doi.org/10.1016/j.cie.2023.109155
  • Rasay, H., F. Naderkhani, and A. M. Golmohammadi. (2022). Reinforcement learning based on stochastic dynamic programming for condition-based maintenance of deteriorating production processes. in 2022 IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE. https://doi.org/1109/ICPHM53196.2022.9815668
  • Drent, C., M. Drent, and J. Arts, (2023). Condition-Based Production for Stochastically Deteriorating Systems: Optimal Policies and Learning. arXiv preprint arXiv: 2308.07507, https://doi.org/10.1287/msom.2022.0473
  • Sun Q, Chen P, Wang X, Ye ZS. (2023). Robust condition‐based production and maintenance planning for degradation management. Production and Operations Management. 32(12): 3951-67. https://doi.org/10.1111/poms.14071
  • Zhang, N., Cai, K., Deng, Y., & Zhang, J. (2024). Joint optimization of condition-based maintenance and condition-based production of a single equipment considering random yield and maintenance delay. Reliability Engineering & System Safety, 241, 109694. https://doi.org/10.1016/j.ress.2023.109694
  • Sutton, R. S. (2018). Reinforcement learning: An introduction. A Bradford Book. https://doi.org/10.1017/S0263574799271172
  • Sun, Q., Chen, P., Wang, X., Ye, Z.-S. (2023). Robust condition-based production and maintenance planning for degradation management. Production and Operations Management 32(12): 3951–3967 https://doi.org/10.1111/poms.14071
نشریه پژوهش های مهندسی صنایع در سیستم های تولید
دوره 12، شماره 25
اسفند 1403
صفحه 31-45

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