Reducing Costs While Maintaining Throughput: Dynamic Coordination of Buffers and Preventive Maintenance
Source abstract
Abstract
Managing variability in modern production systems presents a critical challenge where traditional static maintenance and buffer management policies are often suboptimal. This paper introduces and evaluates an integrated and adaptive control strategy that dynamically combines preventive maintenance (PM) and buffer allocation. Unlike fixed scheduling, the PM policy triggers interventions based on buffer level thresholds and elapsed time, while buffer capacities adapt in real time according to machine blocking percentages. The strategy’s effectiveness was tested via a simulation model of a flow line, using a factorial design to analyze performance across 16 different scenarios. The results show that the fully integrated model provides substantial improvements, with an average reduction in machine processing time of 16.5% and a decrease in unit cost exceeding 50% in optimal configurations compared to the fixed case. Analysis of Variance (ANOVA) also identified buffer capacity and PM frequency as the most influential control parameters. In contrast, configurations optimizing only buffers or only PM yielded modest gains. These findings highlight the powerful synergistic impact of coupling buffer flexibility with adaptive maintenance, demonstrating that responsive, data-driven policies are fundamental to enhancing efficiency and economic competitiveness in Industry 4.0 production systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Queuing Theory Analysis · Reliability and Maintenance Optimization · Scheduling and Optimization Algorithms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Engineering, University of Basilicata, 85100 Potenza, Italy
References (32)
Show source-provided references
1. Kagermann, H., and Wahlster, W. (2022). Ten Years of Industrie 4.0. Sci, 4.10.3390/sci4030026 ↗
2. Designing an adaptive production control system using reinforcement learning10.1007/s10845-020-01612-y ↗
3. Total productive maintenance (TPM) implementation practice: A literature review and directions10.1108/IJLSS-06-2013-0032 ↗
4. Hopp, W.J., and Spearman, M.L. (2011). Factory Physics 2011, Waveland Press.
5. Wang, F., Lu, Y., and Ju, F. (2018, January 20–24). Condition-based real-time production control for smart manufacturing systems. Proceedings of the 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany.10.1109/COASE.2018.8560389 ↗
6. Deep reinforcement learning for condition-based maintenance planning of multi-component systems under dependent competing risks10.1016/j.ress.2020.107094 ↗
7. Adaptive policy of buffer allocation and preventive maintenance actions in unreliable production lines10.1007/s40092-018-0301-7 ↗
8. Joint optimization of part quality inspection planning, buffer allocation, and preventive maintenance in a serial manufacturing10.1142/S0219686725500209 ↗
9. A cyber-physical systems architecture for industry 4.0-based manufacturing systems
10. MINLP model for reliability optimization of system design and maintenance based on markov chain representation10.1016/B978-0-12-823377-1.50177-4 ↗
11. Performance evaluation of the croissant production line with reparable machines10.1007/s40092-014-0087-1 ↗
12. Reliability analysis of repairable systems using system dynamics modeling and simulation10.1007/s40092-014-0069-3 ↗
13. Remaining Useful Life prediction and challenges: A literature review on the use of Machine Learning Methods10.1016/j.jmsy.2022.05.010 ↗
14. Evaluation methodology for preventive maintenance in multi-state manufacturing systems considering different costs10.1080/00207543.2022.2127163 ↗
15. Performance analysis of a production system with two deteriorating machines and a finite buffer10.1007/s10100-023-00871-3 ↗
16. A novel algorithm for optimal buffer allocation in automated asynchronous unreliable lines10.1007/s00170-022-09867-w ↗
17. Deep reinforcement learning for maintenance optimization of a scrap-based steel production line10.1016/j.ress.2024.110199 ↗
18. The buffer allocation problem in production lines: Formulations, solution methods, and instances10.1080/24725854.2018.1442031 ↗
19. Fitness landscapes of buffer allocation problem for production lines with unreliable machines10.1016/j.cor.2024.106819 ↗
20. Zhao, Z., Goodall, P., West, A., Colligan, A., Friel, I., Hickinbotham, S., Price, M., and Jin, Y. (2023, January 8–10). A bio-inspired evolution-development method for modelling and optimisation of buffer allocation in unreliable serial production line. Proceedings of the 3rd International Conference on Mechanical, Aerospace and Automotive Engineering (CMAAE 2023), Nanjing, China.10.1049/icp.2024.0636 ↗
21. Li, L., Dong, M., and Zhou, J. (2024). A Recursive Optimization Approach for Buffer Allocation in Large Production Lines. Appl. Sci., 14.10.3390/app14219953 ↗
22. A scale transformation algorithm for buffer allocation in production lines with unreliable machines10.1016/j.cie.2025.110918 ↗
23. A model review on joint optimization of part quality inspection planning, buffer allocation, and preventive maintenance in SMMS10.1142/S0219686723500312 ↗
24. Buffer allocation and preventive maintenance optimization in unreliable production lines10.1007/s10845-014-0963-y ↗
25. An efficient simulation optimization methodology to solve a multi-objective problem in unreliable unbalanced production lines10.1016/j.eswa.2019.112836 ↗
26. Hung, Y.-H., Shen, H.-Y., and Lee, C.-Y. (2024). Deep reinforcement learning-based preventive maintenance for repairable machines with deterioration in a flow line system. Ann. Oper. Res., 1–21.10.1007/s10479-024-06207-x ↗
27. Buffer allocation problem and preventive maintenance planning in non-homogenous unreliable production lines10.1007/s00170-016-9744-4 ↗
28. Bányai, Á., and Bányai, T. (2022). Real-time maintenance policy optimization in manufacturing systems: An energy efficiency and emission-based approach. Sustainability, 14.10.3390/su141710725 ↗
29. Opportunistic maintenance modeling for series production systems based on bottleneck by considering energy consumption and market demand
30. Maintenance optimization in industry 4.010.1016/j.ress.2023.109204 ↗
31. Elsayed, E.A. (2023). Reliability, Maintainability, Safety, and Sustainability. Springer Handbook of Automation, Springer International Publishing.10.1007/978-3-030-96729-1_31 ↗
32. The impact of preventive maintenance on system cost and buffer size10.1016/0377-2217(95)00313-4 ↗