Design of a Fuzzy Multi-Objective Mathematical Model for Short-Term Preventive Maintenance Scheduling with a Total Productive Maintenance Approach
Keywords:
Fuzzy optimization, multi-objective mathematical model, preventive maintenance scheduling, Total Productive MaintenanceAbstract
The increasing energy consumption in the industrial sector and the consequences associated with greenhouse gas emissions have intensified the need for innovative approaches that simultaneously manage production, cost, and environmental considerations. In this context, production planning and machine scheduling, together with preventive maintenance, play a significant role in reducing energy consumption, increasing productivity, improving quality, and minimizing unexpected downtime. Accordingly, the present study was conducted with the aim of developing a comprehensive model for the simultaneous optimization of production and maintenance processes under uncertainty. This research is applied in nature and proposes a multi-objective mathematical model for production scheduling and preventive maintenance implementation. The primary objectives of the proposed model include minimizing production completion time, reducing costs, decreasing environmental pollutant emissions, and maximizing product quality. Furthermore, to better reflect real-world conditions, uncertainty in model parameters is addressed through a fuzzy approach. To solve the model, an exact optimization method was employed in GAMS software for small-sized problems, while the NSGA-II metaheuristic algorithm was implemented in the MATLAB environment for large-scale problems. The results demonstrated that the proposed approach effectively balances the conflicting objectives of the problem and is capable of generating efficient and reliable solutions. Moreover, the performance evaluation of the NSGA-II algorithm using indicators such as the number of Pareto solutions, solution quality, distance from the ideal point, and solution diversity indicated that the algorithm exhibits desirable efficiency and robustness in large-scale instances. Overall, the findings suggest that integrating production planning with preventive maintenance while incorporating environmental considerations can improve the performance of manufacturing systems and contribute to the advancement of sustainable production.
References
Asadkhani, J., Fallahi, A., & Mokhtari, H. (2022). A Sustainable Supply Chain under VMI-CS Agreement with Withdrawal Policies for Imperfect Items. Journal of Cleaner Production, 376, 134098. https://doi.org/10.1016/j.jclepro.2022.134098
Azimian, M., Karbassian, M., & Atashgar, K. (2021). Selecting the Best Preventive Maintenance Period for Single-Purpose Equipment: A New Fuzzy Decision-Making Approach. Research in Production and Operations Management, 12(4), 21-39. https://doi.org/10.22108/jpom.2021.130857.1401
Bok, Y., Lee, N. K., Jo, S., Lee, S., Kweon, S. J., & Na, H. S. (2024). The Production Scheduling Problem Employing Non-Identical Parallel Machines with Due Dates Considering Carbon Emissions and Multiple Types of Energy Sources. Expert Systems with Applications, 238, 121990. https://doi.org/10.1016/j.eswa.2023.121990
Cheng, J., Chu, F., Chu, C., & Xia, W. (2015). Bi-Objective Optimization of Single-Machine Batch Scheduling under Time-of-Use Electricity Prices. Rairo - Operations Research, 50. https://doi.org/10.1051/ro/2015063
Damchi, Y., & Saebi, J. (2023). A Risk-Based Preventive Maintenance Planning Model Considering Intangible Risk Indicators. Tabriz Journal of Electrical Engineering, 53(3), 171-182. https://doi.org/10.22034/tjee.2023.53875.4540
Duan, J., Feng, M., & Zhang, Q. (2022). Energy-Efficient Collaborative Scheduling of Heterogeneous Multi-Stage Hybrid Flowshop for Large Metallic Component Manufacturing. Journal of Cleaner Production, 375, 134148. https://doi.org/10.1016/j.jclepro.2022.134148
Eshtehadi, R., Fathian, M., & Demir, E. (2017). Robust Solutions to the Pollution-Routing Problem with Demand and Travel Time Uncertainty. Transportation Research Part D: Transport and Environment, 51, 351-363. https://doi.org/10.1016/j.trd.2017.01.003
Fan, Y. V., Perry, S., Klemeš, J. J., & Lee, C. T. (2018). A Review on Air Emissions Assessment: Transportation. Journal of Cleaner Production, 194, 673-684. https://doi.org/10.1016/j.jclepro.2018.05.151
Fanourakis, D. (2025). Climate Change Impacts on Greenhouse Horticulture in the Mediterranean Basin: Challenges and Adaptation Strategies. Plants, 14(21), 3390. https://doi.org/10.3390/plants14213390
Foumani, M., & Smith-Miles, K. (2019). The Impact of Various Carbon Reduction Policies on Green Flowshop Scheduling. Applied Energy, 249, 300-315. https://doi.org/10.1016/j.apenergy.2019.04.155
Hasani, A. A. (2018). A Hybrid Multi-Objective Metaheuristic Algorithm for the Distributed Re-Entrant Permutation Flow Shop Scheduling Problem Considering Preventive Maintenance under Uncertainty. Research in Production and Operations Management, 9(2), 1-22. https://doi.org/10.22108/jpom.2018.92504.0
Lotfi, R. N. A. S. M. M. (2020). Simultaneous Demand-Based Inspection and Preventive Maintenance Planning under Markovian Machine Deterioration for Application in Wind Turbines. Journal of Mechanical Engineering, 18(61), 63-84. https://doi.org/10.22075/jme.2020.19265.1820
Lotfinejad, P., Tarkashvand, A., & Sanaieian, H. (2025). A computational approach for integration of greenhouse and “Shanashir” to enhance thermal comfort of occupants, utilizing NSGA-II algorithm. Building and Environment, 273. https://doi.org/10.1016/j.buildenv.2025.112717
Lu, C., Gao, L., Li, X., Pan, Q., & Wang, Q. (2017). Energy-Efficient Permutation Flow Shop Scheduling Problem Using a Hybrid Multi-Objective Backtracking Search Algorithm. Journal of Cleaner Production, 144, 228-238. https://doi.org/10.1016/j.jclepro.2017.01.011
Moazzam Jozi, A., Tavakkoli-Moghaddam, R., & Abdollahzadeh Sangroodi, H. (2023). A Mathematical Programming Model for Dynamic Grouping of Maintenance Activities Considering Intermittent Operation of a Multi-Component System. Industrial Engineering and Management, 39(1), 3-21. https://doi.org/10.24200/j65.2022.56610.2163
Molaverdi, N., Mousavizadegan, F., & Mehdinia, B. (2020). A Method for Optimizing Preventive Maintenance. Research in Production and Operations Management, 11(3), 117-137. https://doi.org/10.22108/jpom.2021.127885.1354
Moons, S., Ramaekers, K., Caris, A., & Arda, Y. (2017). Integrating Production Scheduling and Vehicle Routing Decisions at the Operational Decision Level: A Review and Discussion. Computers & Industrial Engineering, 104, 224-245. https://doi.org/10.1016/j.cie.2016.12.010
Ning, T., & Huang, Y. (2021). Low Carbon Emission Management for Flexible Job Shop Scheduling: A Study Case in China. Journal of Ambient Intelligence and Humanized Computing, 14. https://doi.org/10.1007/s12652-021-03330-6
Ning, T., Wang, Z., Zhang, P., & Gou, T. (2020). Integrated Optimization of Disruption Management and Scheduling for Reducing Carbon Emission in Manufacturing. Journal of Cleaner Production, 263, 121449. https://doi.org/10.1016/j.jclepro.2020.121449
Nouri, R., Sadeghieh, A., & Lotfi, M. (2020). A Mathematical Model for Simultaneous Planning of Inspections and Preventive Maintenance under Markovian Machine Deterioration and Demand Uncertainty. Industrial Engineering and Management, 35.1(2.2), 151-163. https://doi.org/10.24200/j65.2019.50720.1866
Piroozfard, H., Wong, K. Y., & Wong, W. P. (2017). Minimizing Total Carbon Footprint and Total Late Work Criterion in Flexible Job Shop Scheduling by Using an Improved Multi-Objective Genetic Algorithm. Resources, Conservation and Recycling, 128, 267-283. https://doi.org/10.1016/j.resconrec.2016.12.001
Qamhan, M., Qamhan, A., Al-Harkan, I., & Alotaibi, Y. (2019). Mathematical Modeling and Discrete Firefly Algorithm to Optimize Scheduling Problem with Release Date, Sequence-Dependent Setup Time, and Periodic Maintenance. Mathematical Problems in Engineering, 2019, 1-16. https://doi.org/10.1155/2019/8028759
Raeisi, S. M. A. D. M. T. S. (2019). Simultaneous Planning of Maintenance and Spare Parts Inventory Control: A Case Study of the Paint Pre-Treatment Shuttle Robot in an Automotive Company. Strategic Management in Industrial Systems, 14(49), 19-33.
Sajjadi, M. D. F. A. S. M. (2022). A Simulation-Optimization Model for Networked Failure-Prone Production Systems with a Reliability-Based Maintenance Approach and Revenue Sharing. Journal of Industrial Management Perspective, 12(4), 131-158. https://doi.org/10.52547/jimp.12.4.131
Shahanaghi, K., Jafarian, M., Beikverdi, M., & Nejad Biglari, Z. (2010). Optimization of the Threshold of Preventive Maintenance Actions in a Condition-Based Maintenance Program (CPM) Using a Dynamic Programming Approach. Industrial Engineering and Management, 26(2), 115-119. https://sjie.journals.sharif.edu/article_5185_232f7856250e37cfef4de502d40ed76d.pdf
Sharifzadegan, M., Sohrabi, T., & Jafarnejad Chaghoushi, A. (2022). A Bi-Objective Hybrid Production Scheduling Model with Resource Constraints Using a Preventive Maintenance Approach. Decision Making and Operations Research, 6(Special Issue), 1-17. https://doi.org/10.22105/dmor.2021.278207.1343
Sun, Z., & Li, L. (2013). Opportunity Estimation for Real-Time Energy Control of Sustainable Manufacturing Systems. IEEE Transactions on Automation Science and Engineering, 10(1), 38-44. https://doi.org/10.1109/TASE.2012.2216876
Wang, J., Yao, S., Sheng, J., & Yang, H. (2019). Minimizing Total Carbon Emissions in an Integrated Machine Scheduling and Vehicle Routing Problem. Journal of Cleaner Production, 229, 1004-1017. https://doi.org/10.1016/j.jclepro.2019.04.344
Zhang, L., Wang, J., & You, J. (2015). Consumer Environmental Awareness and Channel Coordination with Two Substitutable Products. European Journal of Operational Research, 241(1), 63-73. https://doi.org/10.1016/j.ejor.2014.07.043
Zou, X., Liu, L., Li, K., & Li, W. (2018). A Coordinated Algorithm for Integrated Production Scheduling and Vehicle Routing Problem. International Journal of Production Research, 56(15), 5005-5024. https://doi.org/10.1080/00207543.2017.1378955
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2026 Foroud Rajabpour (Author); Masoomeh Zeinalnezhad; Vahid Hajipour (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
