ارایه یک مدل برنامه ریزی ریاضی فازی به منظور تخصیص و زمانبندی انجام قطعات در یک سیستم تولید انعطاف پذیر (FMS) و تاثیر تعمیرات و نگهداری بر کیفیت محصول

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی صنایع، واحد تهران شمال، دانشگاه آزاد اسلامی، تهران، ایران.

چکیده
هدف: این پژوهش با هدف توسعه یک مدل ریاضی برای زمان‌بندی تولید کارگاهی انعطاف‌پذیر انجام شده است. تمرکز اصلی بر بهینه‌سازی سه هدف: زمان تکمیل سفارش‌ها، بار کاری حداکثری ماشین‌ها و مجموع بار کاری است. هدف نهایی افزایش بهره‌وری و انعطاف‌پذیری در سیستم‌های تولیدی است.
روش‌شناسی پژوهش: از دو الگوریتم فرا ابتکاری NSGA-II و MOGWO برای حل مدل استفاده شده است. ابتدا مدل در مقیاس کوچک اعتبارسنجی شد و سپس در ابعاد بزرگ‌تر تحلیل حساسیت انجام گرفت. مقایسه عملکرد الگوریتم‌ها با شاخص‌های دقت و کیفیت راه‌حل‌ها صورت گرفت.
یافته‌ها: نتایج نشان داد MOGWO در مسایل متوسط عملکرد بهتری دارد، ولی در مسایل بزرگ تفاوت معناداری با NSGA-II ندارد. بیشترین حساسیت اهداف نسبت به هزینه ساخت و نگهداری مشاهده شد. همچنین الگوی تخصیص منابع و ترتیب بهینه فعالیت‌ها استخراج گردید.
اصالت/ارزش‌افزوده علمی: اصالت این پژوهش در توسعه و کاربرد یک مدل ریاضی ترکیبی برای زمان‌بندی سیستم‌های تولید انعطاف‌پذیر با چندین هدف متضاد و در نظر گرفتن محدودیت‌های واقعی از جمله هزینه‌ها و منابع تولیدی است. همچنین استفاده هم‌زمان از دو الگوریتم NSGA-II و MOGW و مقایسه دقیق عملکرد آن‌ها در ابعاد مختلف، نوآوری دیگری از این تحقیق محسوب می‌شود. ارایه یک الگوی عملیاتی برای توالی فعالیت‌ها نیز به کاربردی‌تر شدن نتایج پژوهش در محیط‌های صنعتی کمک می‌کند.

کلیدواژه‌ها


عنوان مقاله English

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

نویسندگان English

Jafar Hassan Beigi
Meghdad Jahromi
Mohammad Taghipour
Department of Industrial Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran.
چکیده English

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.

کلیدواژه‌ها English

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