A Markov-based Optimal Maintenance Policy for Production Process
DOI:
https://doi.org/10.13052/jrss0974-8024.1817Keywords:
Discrete-time Markov chain, perfect replacement, optimum policy, maintenanceAbstract
This article considers a machine maintenance problem. When the machine fails after a stochastic period, reducing its capacity to a proportion of the nominal level. In this degraded capacity state, three maintenance and repair policy include, continue at 50% capacity, imperfect maintenance and increase the capacity of the machine to 80% or perfect replacement and increase the capacity of the machine to the initial stateare available for evaluation. By modeling the system as a discrete-time Markov chain and analyzing the probability transition matrix between the system states, the costs associated in each state can be evaluated. The objective function representing the average cost per unit time of production is calculated to determine the optimal maintenance policy.
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Azadeh A, Sheikhalishahi M, Khalili M, Firouzi M. An integrated fuzzy simulation fuzzy data envelopment analysis approach for optimum Maintenance planning. International Journal of Computer Integrated Manufacturing 2013; 27(2): 181–199, https://doi.org/10.1080/0951192X.2013.812804.
Gopalakrishnan M, Bokrantz J, Skoog A. Planning of maintenance activities: A current state mapping in industry. Procedia CIRP 2015; 30: 480–485. https://doi.org/10.1016/j.procir.2015.02.093.
Wan S, Gao J, Li J, Tong Y. Web-based Process Planning for Machine Tool Maintenance and Services. Procedia CIRP 2015; 38:165–170. https://doi.org/10.1016/j.procir.2015.07.018.
Angius A, Colledani M, Silipo M, Yamane A. Impact of Preventive Maintenance on the service level of multi-stage manufacturing systems with degrading machines. Journal of the International Federation of Manufacturers and Engineers 2016; 49: 568–573. https://doi.org/10.1016/j.ifacol.2016.07.696.
Elseyi M, Unal A. An integrated heuristic and mathematical modeling method to optimize vehicle maintenance schedule under single dead-end track parking and service level agreement. Computers and Operations Research 2021; 132: 1–16. https://doi.org/10.1016/j.cor.2021.105261.
Ferretti C, Andrews A, Pesenti R. A mathematical programming model to select maintenance strategies in railway networks. Reliability Engineering and System Safety 2016; 1–13. https://doi.org/10.1016/j.ress.2021.107940.
Qiu S, Ming X, Sallak M, Lu J. Joint optimization of production and condition based maintenance scheduling for make-to-order manufacturing systems. Computers and Industrial Engineering 2021; 162: 1–17. https://doi.org/10.1016/j.cie.2021.107753.
Zhang Z, Liu M, Xie M, Dong P. a mathematical programing–based heuristic for coordinated hydrothermal generator maintenance scheduling and long-term unit commitment. Electrical Power and Energy Systems 2022; 147: 1–13. https://doi.org/10.1016/j.ijepes.2022.108833.
Li Y, He X, Huai J. Risk analysis and maintenance decision making of natural gas pipelines with external corrosion based on Bayesian network. Petroleum Science 2021; 19: 1250–1261. https://doi.org/10.1016/j.petsci.2021.09.016.
Fallahnezhad M S, Mostafaeipour A, Sajadieh M. Implementation of traditional (SR)-based PM method with Bayesian inference. International Journal of Industrial Engineeringand Production Research, 2014; 25: 27–32. http://ijiepr.iust.ac.ir/article-1-296-fa.html.
Allal A, Sahnoun M, Adjoudj R, Benslimane S, Mazar M. Multiagent based simulation-optimization of maintenance routing in offshore wind farms. Computers and Industrial Engineering 2021; 157: 1–13. https://doi.org/10.1016/j.cie.2021.107342.
Hong S, Partellus A, Go M, Zhou J. a reliability-and-cost-based fuzzy approach to optimize preventive maintenance scheduling for offshore wind farms. Mechanical Systems and Signal Processing 2019; 124: 643–663. https://doi.org/10.1016/j.ymssp.2019.02.012.
Gan S, Zhang Z, Zhou Y, Shi J. Joint optimization of maintenance, buffer, and spare parts Parts for a production system. Mathematical Modeling 2015;39: 6032–6042. https://doi.org/10.1016/j.apm.2015.01.035.
Diallo C, Khatab A, Venkata U. Developing an objective imperfect selective maintenance optimization model for multicomponent systems. IFAC Papers Online 2019; 52(13): 1079–1084. https://doi.org/10.1016/j.ifacol.2019.11.339.
Martinod R, Historic O, Reg N. Maintenance policy optimization for multicomponent systems considering degradation of components and imperfect maintenance action. Computers and Industrial Engineering 2018; 124: 100–112. https://doi.org/10.1016/j.cie.2018.07.019.
Ghorbani M, Nourelfath M, Gendrei M. a two-stage stochastic programing model for selective maintenance optimization. Reliability Engineering and System Safety 2022; 223: 1–14. https://doi.org/10.1016/j.ress.2022.108480.
Kuo Y. Optimal adaptive control policy for joint machine maintenance and product quality control. European Journal of Operational Research 2006; 171: 586–597. https://doi.org/10.1016/j.ejor.2004.09.022.
Fallahnezhad M S, Nikki S T. A new machine replacement policy based on number of defective items and Markov chains. Iranian Journal of Operations Research 2011; 2: 17–28.
Andersen J, Andersen A, Nielsen B. A numerical study of Markov decision process algorithms for multicomponent replacement problems. European Journal of Operational Research 2022; 299: 898–909. https://doi.org/10.1016/j.ejor.2021.07.007.
Jin H, Han F, Sang Y. An optimal maintenance strategy for multistate deterioration systems based on a semi-Markov decision process coupled with simulation technique. Mechanical Systems and Signal Processing 2020; 139: 1–22. https://doi.org/10.1016/j.ymssp.2019.106570.
Pricopie A, Frangu L, Vilaniva R. Caraman, Caraman S. A preventive maintenance strategy for an actuator using Markov models. IFAC Papers Online 2020; 53(2): 784–789. https://doi.org/10.1016/j.ifacol.2020.12.831.
Comaserica C, Asgardhr S. Optimum Maintenance Policy Using Semi-Markov Decision Processes. Electric Power Systems Research 2009; 79: 1286–1291. https://doi.org/10.1016/j.epsr.2009.03.008.
Amari S, McLaughlin L, Sham H. Cost-Effective Condition-Based Maintenance Using Markov Decision Processes. Reliability and Maintainability Symposium 2006; 06. https://doi.org/10.1109/RAMS.2006.1677417.
Roman F, Kraijema S, Godjevac M, Lodowick G. Optimizing preventive maintenance policy: A data-driven application for a light rail braking system. ProcInstMechEng 2017; 231(5):534–45. https://doi.org/10.1177/1748006X17712662.
Kamal G, Aly M F, Mohib A, Affy IH. Optimization of a multilevel integrated preventive maintenance scheduling mathematical model using genetic algorithm. Int J Manage Sci Eng. Manage 2020; 15(4):247–57. https://doi.org/10.1080/17509653.2020.1726834.
Lin D, Zuo M J, Yam R. Sequential imperfect preventive maintenance models with two categories of failure modes. Nav. Res. Legist 2001; 48 (2): 172–183. https://doi.org/10.1002/1520-6750(200103)48:2%3C172::AID-NAV5%3E3.0.CO;2-5.
Shi Y, Xiang Y, Li M. Optimal maintenance policies for multilevel preventive maintenance with complex effects, IISE Trans 2019; 51 (9): 999–1011. https://doi.org/10.1080/24725854.2018.1532135.
Sarkar B, Fair T. Minimizing maintenance cost for offshore wind turbines following multilevel opportunistic preventive strategy. Renewable Energy 2016; 85: 104–113. https://doi.org/10.1016/j.renene.2015.06.030.
Tajiani B, Vatn J, Naser M. Optimizing the maintenance threshold in presence of shocks: A numerical framework for systems with non-monotonic degradation. Reliability Engineering and System Safety 2024; https://doi.org/10.1016/j.ress.2024.110039.
Li B, Ran Y, Chen B, Chen F, Cai C, Zhang G. Opportunistic maintenance strategy optimization considering imperfect maintenance under hybrid unit-level maintenance strategy. Computers & Industrial Engineering 2023; https://doi.org/10.1016/j.cie.2023.10962.
Ziółkowski J, Oszczypała M, Lêgas A, Konwerski J, Małachowski J, A method for calculating the technical readiness of aviation refuelling vehicles, EksploatacjaiNiezawodnosc – Maintenance and Reliability 2024: 26(3), http://doi.org/10.17531/ein/187888.
Oszczypała M, Ziółkowski J, Małachowski J, Semi-Markov approach for reliability modelling of light utility vehicles. EksploatacjaiNiezawodnosc – Maintenance and Reliability 2023: 25(2), http://doi.org/10.17531/ein/161859.
Knopi K L, MigAw A K. Semi-Markov system model for minimal repair maintenance. Eksploatacjainiezawodnosc – Maintenance and Reliability 2019; 21(2): 256–260, http://dx.doi.org/10.17531/ein.2019.2.9.
Kumar, Pardeep, and Amit Kumar. “Time dependent performance analysis of a Smart Trash bin using state-based Markov model and Reliability approach.” Cleaner Logistics and Supply Chain 9 (2023): 100122. https://doi.org/10.1016/j.clscn.2023.100122.
Kumar, Pardeep, and Amit Kumar. “Quantifying reliability indices of garbage data collection IOT-based sensor systems using Markov birth-death process.” International Journal of Mathematical, Engineering and Management Sciences 8.6 (2023): 1255.
Kumar, Amit, and Pardeep Kumar. “Reliability assessment for multi-state automatic ticket vending machine (ATVM) through software and hardware failures.” Journal of Quality in Maintenance Engineering 28.2 (2022): 448–473.
Bowling, Shannon R., et al, A Markovian approach to determining optimum process target levels for a multi-stage serial production system, European Journal of Operational Research 159.3 (2004): 636–650. https://doi.org/10.1016/S0377-2217(03)00429-6.


