Journal of Quality Engineering and Management

Presenting a fuzzy mathematical programming model for allocating and scheduling parts in a flexible manufacturing system (FMS) and the impact of repairs and maintenance on product quality

Document Type : Original Article

Authors

Jafar Hassan Beigi
Meghdad Jahromi
Mohammad Taghipour

Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.

https://doi.org/10.48313/jqem.2024.215017
Abstract
Purpose: This study aims to develop a mathematical model for flexible job shop scheduling. The primary focus is on optimizing three objectives: the makespan, the maximum machine workload, and the total workload. The ultimate goal is to enhance productivity and flexibility in manufacturing systems.
Methodology: Two metaheuristic algorithms, NSGA-II and MOGWO, were used to solve the model. The model was first validated on a small scale, and then a sensitivity analysis was conducted on larger instances. The performance of the algorithms was compared based on accuracy and solution quality metrics.
Findings: The results indicate that MOGWO performs better on medium-sized problems, whereas in large-scale cases, the difference between the two algorithms is not significant. The highest sensitivity was observed among the objectives regarding production and maintenance costs. Additionally, a resource-allocation pattern and an optimal sequence of operations were derived.
Originality/Value: The originality of this research lies in developing and applying a multi-objective mathematical model for flexible job-shop scheduling that considers real-world constraints, including costs and resource limitations. The simultaneous use and detailed comparison of NSGA-II and MOGWO across different problem sizes is another contribution. Furthermore, the proposed operational pattern improves the applicability of the results in industrial environments.

Keywords

Flexible manufacturing
Gray wolf algorithm
Genetic algorithm
Optimization
[1] Abou Gamila, M., & Motavalli, S. (2003). A modeling technique for loading and scheduling problems in FMS. Robotics and computer-integrated manufacturing, 19(1–2), 45–54. https://doi.org/10.1016/S0736-5845(02)00061-3
[2] Jahromi, M., Tavakkoli-Moghaddam, R., Makui, A., & Saghaei, A. (2017). A new mathematical model for a scheduling problem of dynamic machine-tool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm. Scientia iranica, 24(2), 765–777. https://doi.org/10.24200/sci.2017.4060
[3] Low, C., Yip, Y., & Wu, T. H. (2006). Modelling and heuristics of FMS scheduling with multiple objectives. Computers & operations research, 33(3), 674–694. https://doi.org/10.1016/j.cor.2004.07.013
[4] Celen, M., & Djurdjanovic, D. (2012). Operation-dependent maintenance scheduling in flexible manufacturing systems. CIRP journal of manufacturing science and technology, 5(4), 296–308. https://doi.org/10.1016/j.cirpj.2012.09.005
[5] Jahromi, M. H. M. A., & Tavakkoli-Moghaddam, R. (2012). A novel 0-1 linear integer programming model for dynamic machine-tool selection and operation allocation in a flexible manufacturing system. Journal of manufacturing systems, 31(2), 224–231. https://doi.org/10.1016/j.jmsy.2011.07.008
[6] Chan, F. T. S., & Swarnkar, R. (2006). Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and computer-integrated manufacturing, 22(4), 353–362. https://doi.org/10.1016/j.rcim.2005.08.001
[7] Buyurgan, N., Saygin, C., & Kilic, S. E. (2004). Tool allocation in flexible manufacturing systems with tool alternatives. Robotics and computer-integrated manufacturing, 20(4), 341–349. https://doi.org/10.1016/j.rcim.2004.01.001
[8] Chen, J. H., & Ho, S.Y. (2005). A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm. International journal of machine tools and manufacture, 45(7–8), 949–957. https://doi.org/10.1016/j.ijmachtools.2004.10.010
[9] Nagarjuna, N., Mahesh, O., & Rajagopal, K. (2006). A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system. Robotics and computer-integrated manufacturing, 22(4), 342–352. https://doi.org/10.1016/j.rcim.2005.07.006
[10]   Mahdavi, I., Jazayeri, A., Jahromi, M., Jafari, R., & Iranmanesh, H. (2008). P-ACO approach to assignment problem in FMSs. Proceedings of word academy of science, engineering and technology, 42(5), 196–203. https://www.academia.edu/download/103090636/14341.pdf
[11]   Persi, P., Ukovich, W., Pesenti, R., & Nicolich, M. (1999). A hierarchic approach to production planning and scheduling of a flexible manufacturing system. Robotics and computer-integrated manufacturing, 15(5), 373–385. https://doi.org/10.1016/S0736-5845(99)00034-4
[12]   Bouazza, W., Hamdadou, D., Sallez, Y., & Trentesaux, D. (2019). Manufacturing letters. https://B2n.ir/tj8213
[13]   Lee, C. S., Kim, S. S., & Choi, J. S. (2003). Operation sequence and tool selection in flexible manufacturing systems under dynamic tool allocation. Computers & industrial engineering, 45(1), 61–73. https://doi.org/10.1016/S0360-8352(03)00019-6
[14]   Yang, Y., Huang, M., Wang, Z. Y., & Zhu, Q. B. (2020). Robust scheduling based on extreme learning machine for bi-objective flexible job-shop problems with machine breakdowns. Expert systems with applications, 158, 113545. https://doi.org/10.1016/j.eswa.2020.113545
[1] Abou Gamila, M., & Motavalli, S. (2003). A modeling technique for loading and scheduling problems in FMS. Robotics and computer-integrated manufacturing, 19(1–2), 45–54. https://doi.org/10.1016/S0736-5845(02)00061-3
[2] Jahromi, M., Tavakkoli-Moghaddam, R., Makui, A., & Saghaei, A. (2017). A new mathematical model for a scheduling problem of dynamic machine-tool selection and operation allocation in a flexible manufacturing system: A modified evolutionary algorithm. Scientia iranica, 24(2), 765–777. https://doi.org/10.24200/sci.2017.4060
[3] Low, C., Yip, Y., & Wu, T. H. (2006). Modelling and heuristics of FMS scheduling with multiple objectives. Computers & operations research, 33(3), 674–694. https://doi.org/10.1016/j.cor.2004.07.013
[4] Celen, M., & Djurdjanovic, D. (2012). Operation-dependent maintenance scheduling in flexible manufacturing systems. CIRP journal of manufacturing science and technology, 5(4), 296–308. https://doi.org/10.1016/j.cirpj.2012.09.005
[5] Jahromi, M. H. M. A., & Tavakkoli-Moghaddam, R. (2012). A novel 0-1 linear integer programming model for dynamic machine-tool selection and operation allocation in a flexible manufacturing system. Journal of manufacturing systems, 31(2), 224–231. https://doi.org/10.1016/j.jmsy.2011.07.008
[6] Chan, F. T. S., & Swarnkar, R. (2006). Ant colony optimization approach to a fuzzy goal programming model for a machine tool selection and operation allocation problem in an FMS. Robotics and computer-integrated manufacturing, 22(4), 353–362. https://doi.org/10.1016/j.rcim.2005.08.001
[7] Buyurgan, N., Saygin, C., & Kilic, S. E. (2004). Tool allocation in flexible manufacturing systems with tool alternatives. Robotics and computer-integrated manufacturing, 20(4), 341–349. https://doi.org/10.1016/j.rcim.2004.01.001
[8] Chen, J. H., & Ho, S.Y. (2005). A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm. International journal of machine tools and manufacture, 45(7–8), 949–957. https://doi.org/10.1016/j.ijmachtools.2004.10.010
[9] Nagarjuna, N., Mahesh, O., & Rajagopal, K. (2006). A heuristic based on multi-stage programming approach for machine-loading problem in a flexible manufacturing system. Robotics and computer-integrated manufacturing, 22(4), 342–352. https://doi.org/10.1016/j.rcim.2005.07.006
[10]   Mahdavi, I., Jazayeri, A., Jahromi, M., Jafari, R., & Iranmanesh, H. (2008). P-ACO approach to assignment problem in FMSs. Proceedings of word academy of science, engineering and technology, 42(5), 196–203. https://www.academia.edu/download/103090636/14341.pdf
[11]   Persi, P., Ukovich, W., Pesenti, R., & Nicolich, M. (1999). A hierarchic approach to production planning and scheduling of a flexible manufacturing system. Robotics and computer-integrated manufacturing, 15(5), 373–385. https://doi.org/10.1016/S0736-5845(99)00034-4
[12]   Bouazza, W., Hamdadou, D., Sallez, Y., & Trentesaux, D. (2019). Manufacturing letters. https://B2n.ir/tj8213
[13]   Lee, C. S., Kim, S. S., & Choi, J. S. (2003). Operation sequence and tool selection in flexible manufacturing systems under dynamic tool allocation. Computers & industrial engineering, 45(1), 61–73. https://doi.org/10.1016/S0360-8352(03)00019-6
[14]   Yang, Y., Huang, M., Wang, Z. Y., & Zhu, Q. B. (2020). Robust scheduling based on extreme learning machine for bi-objective flexible job-shop problems with machine breakdowns. Expert systems with applications, 158, 113545. https://doi.org/10.1016/j.eswa.2020.113545
Journal of Quality Engineering and Management
Volume 14, Issue 2
Summer 2024
Pages 105-126

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