Joint Production and Maintenance Planning Given the Effects of Imperfect Maintenance on the Amount of Defective Products

Document Type : Research Paper

Authors

1 Department of Industrial Engineering, University of Kurdistan, Sanandaj, Iran

2 Department of Industrial Engineering, Kermanshah University of Technology, Kermanshah, Iran

Abstract

In order to plan production systems correctly and optimally, it is necessary to examine the maintenance policy, taking into account various factors such as time and cost, production rate and its effects on machine deterioration, etc. In this research, an attempt is made to jointly plan the production and maintenance process in a multi-period single-product system, considering the production capacity limitation and the effect of imperfect maintenance on reducing the probability of producing nonconforming products. For this purpose, by defining a set of states, actions, and rewards appropriate to each action, the problem is modeled in the Markov decision process framework. To determine the best action in each possible state of the problem, the stochastic dynamic programming technique is used. By solving the problem, a decision will be made regarding whether to perform imperfect maintenance and to determine the best production volume for each period. The goal is to determine the maintenance policy and optimal production volume in each period for different states in such a way that total demand can be covered during the allowed production periods at the lowest cost. The results indicate the effectiveness of the model in providing a suitable program for production and maintenance management in the given system. This issue is investigated by solving a numerical example and analyzing the effects of changing parameters on the results of the problem. If the cost of imperfect maintenance exceeds a certain threshold, increasing the probability of producing defective products is preferable to spending maintenance costs.

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Main Subjects


[1] Farahani, Ameneh, and Hamid Tohidi. "Integrated optimization of quality and maintenance: A literature review." Computers & Industrial Engineering 151 (2020): 106924.
[2] Sule, Dileep R. Production planning and industrial scheduling. Taylor & Francis Group, 2008.
[3] Guiras, Zouhour, Sadok Turki, Nidhal Rezg, and Alexandre Dolgui. "Optimal maintenance plan for two-level assembly system and risk study of machine failure." International Journal of Production Research 57, no. 8 (2018): 2446-2463.
[4] Yahyatabar, Ali, and Amir Abbas Najafi. "Condition based maintenance policy for series-parallel systems through Proportional Hazards Model: A multi-stage stochastic programming approach." Computers & Industrial Engineering 126 (2018): 30-46.
[5] Shahrokhi, Mahmoud, and Zahra Sobhani. "Optimization of availability the system with the redundant considering the half-state deals and repair rates change." Journal of Modeling in Engineering 16, no. 54 (2018): 267-281. (in Persian)
[6] Srivastava, Priyank, Dinesh Khanduja, and V. P. Agrawal. "Agile maintenance attribute coding and evaluation based decision making in sugar manufacturing plant." Operational Research 57, no. 4 (2019): 553-583.
[7] Wenbin, Zeng, Ilia Frenkel, Shen Guixiang, Igor Bolvashenkov, Jorg Kammermann, Hans-Georg Herzog, and Lev Khvatskin. "Markov Reward Approach and Reliability Associated Cost Model for Machine Tools Maintenance-Planning Optimization." International Journal of Mathematical, Engineering and Management Sciences 4, no. 4 (2019): 824-840.
[8] Hesam, Abed, Saeed Emami, and Ramezan Nemati Keshteli. "Scheduling of jobs and maintenance activities in an unrelated parallel machines environment." Journal of Modeling in Engineering 17, no. 58 (2019): 233-247. (in Persian)
[9] Dinh, Duc-Hanh, Phuc Do, and Benoit Iung. "Maintenance optimisation for multi-component system with structural dependence: Application to machine tool sub-system." CIRP Annals - Manufacturing Technology 69 (2020): 417-420.
[10] Liu, Bin, Mahesh D. Pandey, Xiaolin Wang, and Xiujie Zhao. "A finite-horizon condition-based maintenance policy for a two-unit system with dependent degradation processes." European Journal of Operational Research  265, no. 2 (2021): 705-717.
[11] Zhang, Ping, Xiaoyan Zhu, and Min Xie. "A model-based reinforcement learning approach for maintenance optimization of degrading systems in a large state space." Computers & Industrial Engineering 161, no. 7 (2021): 107622.
[12] Taji, Jalal, Hiwa Farughi, and Hasan Rasay. "A new approach to preventive maintenance planning considering non-failure stops and failure interdependence between components." Advances in Industrial Engineering 56, no. 2 (2022): 231-249.
[13] Yeardley, Aaron S., Jude O. Ejeh, Louis Allen, Solomon F. Brown, and joan Cordiner. "Integrating machine learning techniques into optimal maintenance scheduling." Computers and Chemical Engineering 166 (2022): 107958.
[14] Yilmaz, Ibrahim, Babek Erdebilli, Mehdi Amine Naji, and Ahmed Mousrij. "A Fuzzy DEMATEL framework for maintenance performance improvement: A case of Moroccan Chemical Industry." Journal of Engineering Research 11 (2023): 100019.
[15] Duffuaa, Salih., Mohamed Idris, Ahmet Kolus, and Umar Al-Turki. "A mathematical model for optimal turnaround maintenance planning and scheduling for a network of plants in process industry supply chain." Computers & Chemical Engineering 180, (2024): 108477.
[16] Santos, Cleiton Ferreira dos, Eduardo de Freitas Rocha Loures, and  Eduardo Alves Portela Santos. "A smart framework to perform a criticality analysis in industrial maintenance using combined MCDM methods and process mining techniques." The International Journal of Advanced Manufacturing Technology 132 (2024).
[17] Yildirim, Mehmet Bayram, and Farnaz Ghazi Nezami. "Integrated maintenance and production planning with energy consumption and minimal repair." The International Journal of Advanced Manufacturing Technology 74 (2014): 1419-1430.
[18] Bajestani, Maliheh Aramon, Dragan Banjevic, and J. Christopher Beck. "Integrated maintenance planning and production scheduling with Markovian deteriorating machine conditions." International Journal of Production Research 52, no. 24, (2014): 7377-7400.
[19] Aghezzaf, El-Houssaine, Phuoc Le Tam, and Abdelhakim Khatab. "Optimizing Production and Imperfect Preventive Maintenance Planning's Integration in Failure-Prone Manufacturing Systems." Reliability Engineering and System Safety 145 (2015): 190-198.
[20] Desforges, Xavier, Mickael Dievart, and Bernard Archimede. "A prognostic function for complex systems to support production and maintenance co-operative planning based on an extension of object oriented Bayesian networks." Computers in Industry 86 (2017): 34-51.
[21] Hajej, Zied, Nidhal Rezg, and Tarek Askri. "Joint optimization of capacity, production andmaintenance planning of leasedmachines." Journal of Intelligent Manufacturing 31 (2018): 351-374.
[22] Alimian, Mahyar, Mohammsd Saidi-Mehrabad, and Armin Jabbarzadeh. "A robust integrated production and preventive maintenance planning model for multi-state systems with uncertain demand and common cause failures." Journal of Manufacturing Systems 50 (2019): 263-277.
[23] Xu, Shengliang, Wenquan Dong, Mingzhou Jin,and Liya Wang. "Single-Machine Scheduling with Fixed or Flexible Maintenance." Computers & Industrial Engineering 139 (2019): 106203.
[24] Qinming, Liu, Dong Ming, Chen F.F., Lv Wenyuan, and Ye Chunming. "Single-machine-based joint optimization of predictive maintenance planning and production scheduling." Robotics and Computer Integrated Manufacturing 51 (2018): 238-247.
[25] Liu, Bin, Kangzhe He, and Min Xie. "Integrated production and maintenance planning for a deteriorating system under uncertain demands." IFAC-PapersOnLine 53, no. 3 (2020): 222-226.
[26] Uit Het Broek, Michiel A.J., Ruud H. Teunter, Bram de Jonge, and Jasper Veldman. "Joint condition-based maintenance and condition-based production optimization." Reliability Engineering and System Safety 214 (2021): 107743.
[27] Rivera-Gomez, Hector, Ali Gharbi, Jean-Pierre Kenne, Ruth Ortiz-Zarco, and Jose Ramon Corona-Armenta. "Joint production, inspection and maintenance control policies for deteriorating system under quality constraint." Journal of Manufacturing Systems 60 (2021): 585-607.
[28] Sharifi, Mani, and Sharareh Taghipour. "Optimal production and maintenance scheduling for a degrading multi-failure modes single-machine production environment." Applied Soft Computing 106 (2021): 107312.
[29] Jiang, Junwei, Youjun An, Yuanfa Dong, Jiawen Hu, Yinghe Li, and Ziye Zhao. "Integrated optimization of non-permutation flow shop scheduling and maintenance planning with variable processing speed." Reliability Engineering & System Safety 234 (2023): 109143.
[30] Leo, Egidio, and Sebastian Engell. "Handling Type-I and Type-II endogenous uncertainties in simultaneous production planning and condition-based maintenance optimization in continuous production." Computers and Chemical Engineering 174 (2023): 108227.
[31] Bahou, Ziyad, Mohamed Reda Lemnaouar, and Issam Krimi. "Integrated non-cyclical preventive maintenance scheduling and production planning for multi-parallel component production systems with interdependencies-induced degradation." The International Journal of Advanced Manufacturing Technology 130 (2024): 4723-4749.
[32] Zhang, Nan, Kaiquan Cia, Yingjun Deng, and Jun Zhang. "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 (2024): 109694.
[33] Derhami, Vali, Farinaz Alamiyan Harandi, and Mohammad Bagher Dowlatshahi. Reinforcement learning. Yazd University Pres, 2017. (in Persian)
[34] Koopmans, Marco, and Bram de Jonge. "Condition-based maintenance and production speed optimization under limited maintenance capacity." Computers & Industrial Engineering 179 (2023): 109155.
[35] El Hami, Abdelkhalak, and Bouchaib Radi. Optimization and programming. Wiley & Sons, 2021.