Abstract




 
   

IJE TRANSACTIONS B: Applications Vol. 31, No. 11 (November 2018) 1920-1927    Article in Press

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  A POMDP FRAMEWORK TO FIND OPTIMAL INSPECTION AND MAINTENANCE POLICIES VIA AVAILABILITY AND PROFIT MAXIMIZATION FOR MANUFACTURING SYSTEMS
 
R. Ghandali, M. H. Abooeie and M. S. Fallah Nezhad
 
( Received: April 07, 2018 – Accepted: October 26, 2018 )
 
 

Abstract    Abstract: Maintenance can be the factor of either increasing or decreasing system's availability, so it is valuable work to evaluate a maintenance policy from cost and availability point of view, simultaneously and according to decision maker's priorities. This study proposes a Partially Observable Markov Decision Process (POMDP) framework for a partially observable and stochastically deteriorating system in which inspection and maintenance optimal policies of Condition Based Maintenance (CBM) must be determined to maximize the average profit and availability of the system simultaneously. A recent exact method named Accelerated Vector Pruning method (AVP) and some other popular estimatingand exact methods are applied and compared in solving such problems.

 

Keywords    Availability maximization, profit maximization, Condition Based Maintenance, Partially Observable Markov Decision Process, stochastically deteriorating, uncertain monitoring, manufacturing systems, Accelerated Vector Pruning

 

چکیده    هزينه‌هاي نگهداري و تعميرات (نت)، بخش عمده‌اي از هزينه‌هاي توليد را تشکيل مي‌دهد و برنامه‌ريزي براي بهينه‌سازي آن جزو اولويت‌هاي اصلي صنايع مختلف است. از سوي ديگر در دنياي رقابتي امروز توليد محصول با دسترس‌پذيري بالا يک اولويت مهم به شمار مي‌آيد، خصوصا در سيستم‌هايي که اثرات مخرب خرابي‌هاي منجر به توقف و عدم دسترس‌پذيري جدي هستند. از آنجا که نت به‌عنوان فعاليتي هزينه‌بر و در عين حال سود آور مي‌تواند هم عامل افزايش دسترس‌پذيري سيستم باشد و هم عامل کاهش آن، ارزيابي يک سياست نت از لحاظ هزينه و دسترس‌پذيري به‌طور همزمان و با توجه به اولويت‌هاي فرد تصميم گيرنده و ارائه‌ي يک برنامه‌ريزي جامع براي ايجاد توازن بهينه بين اهداف مذکور مي‌تواند بسيار ارزشمند باشد. به‌طور خلاصه مي‌توان گفت اين مطالعه چارچوبي نوين براي برنامه‌ريزي رياضي مسئله‌ي نت وضعيت محور در قالب مدل مارکف، در مورد تجهيزات قابل مشاهده‌ي جزئي رو به زوال تصادفي در سيستم‌هاي توليدي، با در نظر گرفتن فاکتورهاي هزينه و دسترس‌پذيري و نيز روابط ما بين آنها، در جهت ارائه‌ي سياست بهينه‌ي بازرسي و نت، پيشنهاد مي‌نمايد. برخي تکنيک‌هاي رياضي از قبيل "فرآيند تصميم‌گيري مارکف قابل مشاهده جزئي" و تئوري بيز براي مدل‌سازي مسئله تصادفي به‌کار گرفته شده‌اند. مدل‌سازي مسئله نت مبتني بر وضعيت با محوريت هزينه-دسترس‌پذيري در چارچوب "فرآيند تصميم‌گيري مارکف قابل مشاهده جزئي" کار نويني است که منجر به ارائه‌ي بهتر طبيعت "قابل مشاهده جزئي" بودن و "زوال تصادفي" در بسياري سيستم‌ها مي‌شود. در حل مدل از یک روش جديد که روش دقيقی است استفاده شده و کارايي روش در حل چنين مسائلي با برخي روش‌هاي دقيق و تقريبي ديگر مقايسه شده است.

References    1. Duffuaa, S.O., Ben-Daya, M., Al-Sultan, K.S., and Andijani, A.A., “A generic conceptual simulation model for maintenance systems“, Journal of Quality in Maintenance Engineering, Vol. 7, No. 3, (2001), 207-219. 2. Takata, S., Kirnura, F., Van Houten, F. J. A. M., Westkamper, E., Shpitalni, M., Ceglarek, D., and Lee, J., “Maintenance: changing role in life cycle management“, CIRP Annals-Manufacturing Technology, Vol. 53, No. 2, (2004), 643-655. 3. Ahmad, R., and Kamaruddin, S., “An overview of time-based and condition-based maintenance in industrial application“, Computers & Industrial Engineering, Vol. 63, No. 1, (2012), 135-149. 4. Grall, A., Bérenguer, C., and Dieulle, L., “A condition-based maintenance policy for stochastically deteriorating systems“, Reliability Engineering & System Safety, Vol. 76, No. 2, (2002a), 167-180. 5. Han, Y., and Song, Y. H., “Condition monitoring techniques for electrical equipment-a literature survey“, Power Delivery, IEEE Transactions on, Vol. 18, No. 1, (2003), 4-13. 6. Moya, M. C. C., “The control of the setting up of a predictive maintenance programme using a system of indicators“, Omega, Vol. 32, No. 1, (2004), 57-75. 7. Jardine, A. K., Lin, D., and Banjevic, D., “A review on machinery diagnostics and prognostics implementing condition-based maintenance“, Mechanical systems and signal processing, Vol. 20, No. 7, (2006), 1483-1510. 8. Rasay, H., Fallah Nezhad, M. S., and Zare Mehrjardi, Y., “Application of the multivariate control charts for condition based maintenance“, International Journal of Engineering, Vol. 31, No. 4, (2018), 204-211. 9. Rahimi Komijani, H., Shahin, M., and Jabbarzadeh, A., “Optimal policy of condition-based maintenance considering probabilistic logistic times and the environmental contanination issues“, International Journal of Engineering, Vol. 31, No. 2, (2018), 357-364. 10. Jiang, R., Kim, M.J., and Makis, V., “Availability maximization under partial observations“, OR spectrum, Vol. 35, No. 3, (2013), 691-710. 11. Ghorbani, S., “Reliability analysis for systems subject to degradation and shocks“, PhD diss., Rutgers University-Graduate School-New Brunswick, (2014). 12. Eti, M. C., Ogaji, S. O. T., & Probert, S. D., “Reducing the cost of preventive maintenance (PM) through adopting a proactive reliability-focused culture“, Applied energy, Vol. 83, No. 11, (2006), 1235-1248. 13. Ferreira, R.J., de Almeida, A.T., and Cavalcante C.A., “A multi-criteria decision model to determine inspection intervals of condition monitoring based on delay time analysis“, Reliability Engineering & System Safety, Vol. 94, No. 5, (2009), 905-912. 14. Martorell, S., Carlos, S., Villanueva, J. F., Sanchez, A. I., Galván, B., Salazar, D., and Cepin, M., “Use of multiple objective evolutionary algorithms in optimizing surveillance requirements“, Reliability Engineering & System Safety, Vol. 91, No.9, (2006), 1027-1038. 15. Zio, E., and Viadana, G., “Optimization of the inspection intervals of a safety system in a nuclear power plant by Multi-Objective Differential Evolution (MODE)“, Reliability Engineering & System Safety, Vol. 96, (2011) 1552-1563. 16. Roijers, D. M., Whiteson, S., and Oliehoek, F. A., “Point-Based Planning for Multi-Objective POMDPs“, In IJCAI, (2015), 1666-1672. 17. Kaelbling, L. P., Littman, M. L., and Cassandra, A. R., “Planning and acting in partially observable stochastic domains“, Artificial intelligence, Vol. 101, (1998), 99-134. 18. Alaswad, S., and Xiang, Y., “A review on condition-based maintenance optimization models for stochastically deteriorating system“, Reliability Engineering & System Safety, Vol. 157, (2017), 54-63. 19. Jin, L., Mashita, T., and Suzuki, K., “An optimal policy for partially observable Markov decision processes with non-independent monitors“, Journal of Quality in Maintenance Engineering, Vol. 11, No. 3, (2005), 228-238. 20. Papakonstantinou, K. G., and Shinozuka, M., “Planning structural inspection and maintenance policies via dynamic programming and Markov processes. Part I: Theory“, Reliability Engineering & System Safety, Vol. 130, (2014), 202-213. 21. Kumar A, Meenakshi N, “Marketing management“, Vikas Publishing House. (2011). 22. Ahmadi-Javid, A., and Ghandali, R., “An efficient optimization procedure for designing a capacitated distribution network with price-sensitive demand“, Optimization and Engineering, Vol. 15, No. 3, (2014), 801-817. 23. Pak, P. K., Kim, D. W., and Jeong, B. H., “Machine Maintenance Policy Using Partially Observable Markov Decision Process“, Journal of the KSQC, Vol. 16, (1988). 24. Pineau, J., Gordon, G., and Thrun, S., “Point-based value iteration: An anytime algorithm for POMDPs“, In IJCAI, Vol. 3, (2003), 1025-1032. 25. Spaan, M. T., and Vlassis, N., “Perseus: Randomized point-based value iteration for POMDPs“, Journal of artificial intelligence research, Vol. 24, (2005), 195-220. 26. Qian, W., Liu, Q., Zhang, Z., Pan, Z., and Zhong, S., “Policy graph pruning and optimization in Monte Carlo Value Iteration for continuous-state POMDPs“, In Computational Intelligence (SSCI), 2016 IEEE Symposium Series on IEEE, (2016), 1-8. 27. Walraven, E., and Spaan, M. T. “Accelerated Vector Pruning for Optimal POMDP Solvers“, In AAAI, (2017), 3672-3678. 28. Ahuja. D., www.codeproject.com/Articles/9898/Heap-Walker, (2005). 29. Agrawal, R., Realff, M. J., and Lee, J. H., “MILP based value backups in partially observed Markov decision processes (POMDPs) with very large or continuous action and observation spaces“, Computers & Chemical Engineering, Vol. 56, (2013), 101-113. 30. Smallwood, R. D., and Sondik, E. J., “The optimal control of partially observable Markov processes over a finite horizon“, Operations research, Vol.21, No. 5, (1973), 1071-1088. 31. Özgen, S., and Demirekler, M., “A Fast Elimination Method for Pruning in POMDPs“, In Joint German/Austrian Conference on Artificial Intelligence, (2016), 56-68, Springer International Publishing. 32. Cassandra, A., Littman, M. L., and Zhang, N. L., “Incremental pruning: A simple, fast, exact method for partially observable Markov decision processes“, In Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence (1997), 54-61, Morgan Kaufmann Publishers Inc. 33. White, C. C. “A survey of solution techniques for the partially observed Markov decision process“, Annals of Operations Research, Vol. 32, No. 1, (1991), 215-230. 34. Littman, M. L., “The witness algorithm: Solving partially observable Markov decision processes“, Brown University, Providence, RI, (1994).





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