بررسی و امکان‌پذیری اجرای سیاست‌های مقیاس‌پذیری ظرفیت تولید مطلوب براساس سیستم تولید قابل پیکر بندی مجدد با رویکرد سیستم‌های پویا
الموضوعات :
rohollah ranjbar
1
,
seyed ahmad shayan nia
2
,
amirmehdi miandaragh
3
,
mohammadreza lotfi
4
1 - department of industrial management, islamic azad university,firoozkooh branch,firoozkooh, iran
2 - department of industrial management, firoozkooh branch, firoozkooh , iran
3 - department of mathemathics,islamic azad university,firoozkooh branch, firoozkooh iran
4 - department of industrial engineering,islamic azad university, firoozkooh, iran
تاريخ الإرسال : 16 الخميس , جمادى الأولى, 1442
تاريخ التأكيد : 09 الجمعة , ربيع الأول, 1443
تاريخ الإصدار : 18 الجمعة , جمادى الثانية, 1443
الکلمات المفتاحية:
سیستم های پویا,
مقیاس پذیری ظرفیت تولید,
سیستم تولید قابل پیکربندی مجدد,
ملخص المقالة :
در این تحقیق مدل جدیدی برای ارزیابی سیاست‌های ظرفیتی براساس سفارشات جدید محصولات، موجودی مالی و بودجه بر اساس سیستم های پویا ارائه شده است. قلمرو تحقیق شرکت ملی گاز ایران می باشد. داده‌های به دست آمده از طریق مصاحبه‌های اکتشافی از کارشناسان شرکت ملی گاز ایران انتخاب شد.در ابتدا بر اساس ارزیابی مطالعات نظری، مصاحبه‌های اکتشافی با خبرگان و توسعه چارچوب مفهومی پژوهش انجام شده و پس از بررسی اطلاعات مربوط به رفتار متغیرها، ارتباطات منطقی از نحوه تاثیرگذاری آنها بر روی یکدیگر استخراج شده و پس از بیان فرضیه پویایی، نموار علت معلولی مربوط تهیه شد. پس از ترسیم نمودار حلقه‌های علی و معلولی و به منظور آنالیز و تحلیل پارامترهای دخیل در مدل، نمودار انباشت جریان رسم گردید. مدل طراحی شده اجرا شده و رفتار متغیرها بررسی و سپس اعتبارسنجی مدل انجام شد.مدل شامل چهار بخش سفارشات ، تولید ، تحقیق و توسعه ظرفیت سازی و بخش مالی می باشد.پس از اجرای برنامه ارتباط و تاثیر پارامترها بر یکدیگر مشخص گردید . نتایج نشان داد که شرکت ظرفیت تولید و سطح موجودی خود را با در نظر گرفتن نرخ سفارش دریافتی از سوی مشتریان تنظیم می‌نماید و خروجی محصول خود را با نگرش عدم خروجی قابل ذخیره در نظر گرفت و همانطور که در مدل مشاهده نمودید با این رویکرد توانست تا ظرفیت تولید و سطح موجودی را با نیاز بازار تطابق بخشد.
المصادر:
Abdi, M. R., & Labib, A. W. (2003). A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. International Journal of production research, 41(10), 2273-2299.
Andersen, A. L., Brunoe, T. D., & Nielsen, K. (2015, September). Reconfigurable manufacturing on multiple levels: literature review and research directions. In IFIP International conference on advances in production management systems(pp. 266-273). Springer, Cham.
Andersen, A. L., Brunoe, T. D., & Nielsen, K. (2019). Engineering education in changeable and reconfigurable manufacturing: Using problem-based learning in a learning factory environment. Procedia Cirp, 81, 7-12.
Asghar, E., Baqai, A. A., & Homri, L. (2018). Optimum machine capabilities for reconfigurable manufacturing systems. The International Journal of Advanced Manufacturing Technology, 95(9), 4397-4417.
Ashraf, M., & Hasan, F. (2018). Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology, 98(5), 2137-2156.
Bensmaine, A., Benyoucef, L., and Dahane, D. (2013). A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computers & Industrial Engineering, 66(3), 519–524.
Bortolini, M., Ferrari, E., Galizia, F. G., & Regattieri, A. (2021). An optimisation model for the dynamic management of cellular reconfigurable manufacturing systems under auxiliary module availability constraints. Journal of Manufacturing Systems, 58, 442-451.
Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of manufacturing systems, 49, 93-106.
Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of manufacturing systems, 49, 93-106.
Bortolini, M., Galizia, F. G., & Mora, C. (2019). Dynamic design and management of reconfigurable manufacturing systems. Procedia manufacturing, 33, 67-74.
Choi, Y. C., & Xirouchakis, P. (2015). A holistic production planning approach in a reconfigurable manufacturing system with energy consumption and environmental effects. International Journal of Computer Integrated Manufacturing, 28(4), 379-394.
Deif, A. M., & ElMaraghy, H. A. (2007). Assessing capacity scalability policies in RMS using system dynamics. International journal of flexible manufacturing systems, 19(3), 128-150.
Dou, J., Li, J., and Su, C. (2016). Bi objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II. The International Journal of Advanced Manufacturing Technology, 86(5-8), 1945–1962.
Gao, Guibing., Yue, Wenhui, Wang, Junshen., Ou, Wenchu. (2020). Structural-vulnerability assessment of reconfigurable manufacturing system based on universal generating function, Reliability Engineering & System Safety, 20(3): 101-107.
Haddou Benderbal, H., Dahane, M., & Benyoucef, L. (2017). Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints. International Journal of Production Research, 55(20), 6033-6051.
Hashemi-Petroodi, S. E., Dolgui, A., Kovalev, S., Kovalyov, M. Y., & Thevenin, S. (2021). Workforce reconfiguration strategies in manufacturing systems: a state of the art. International Journal of Production Research, 59(22), 6721-6744.
Khan, A. S., Homri, L., Dantan, J. Y., & Siadat, A. (2020). Cost and quality assessment of a disruptive reconfigurable manufacturing system based on MOPSO metaheuristic. IFAC-PapersOnLine, 53(2), 10431-10436.
Lamy, D., Delorme, X., Lacomme, P., & Fleury, G. (2020). Toward Scheduling for Reconfigurable Manufacturing Systems. IFAC-PapersOnLine, 53(2), 10443-10448.
Lee, S., Ryu, K., & Shin, M. (2017). The development of simulation model for self-reconfigurable manufacturing system considering sustainability factors. Procedia manufacturing, 11, 1085-1092.
Li, J., Wang, A., and Tang, C. (2014). Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 74(1-4), 47–64.
Maganha, I., Silva, C., & Ferreira, L. M. D. (2018). Understanding reconfigurability of manufacturing systems: An empirical analysis. Journal of Manufacturing Systems, 48, 120-130.
Moghaddam, S. K., Houshmand, M., & Fatahi Valilai, O. (2018). Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL). International Journal of Production Research, 56(11), 3932-3954.
Ouaret, S., Kenné, J. P., & Gharbi, A. (2019). Production and replacement planning of a deteriorating remanufacturing system in a closed-loop configuration. Journal of Manufacturing Systems, 53, 234-248.
Petroodi, S. E. H., Eynaud, A. B. D., Klement, N., & Tavakkoli-Moghaddam, R. (2019). Simulation-based optimization approach with scenario-based product sequence in a reconfigurable manufacturing system (RMS): A case study. IFAC-PapersOnLine, 52(13), 2638-2643.
Singh, P. P., Madan, J., & Singh, H. (2020). A systematic approach for responsiveness assessment for product and material flow in reconfigurable manufacturing system (RMS). Materials Today: Proceedings, 28, 1643-1648.
Touzout, F. A., & Benyoucef, L. (2019). Multi-objective multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid metaheuristics. International Journal of Production Research, 57(24), 7520-7535.
Youssef, A. M., & ElMaraghy, H. A. (2008). Performance analysis of manufacturing systems composed of modular machines using the universal generating function. Journal of manufacturing systems, 27(2), 55-69.
Zhang, Y., Zhao, M., Zhang, Y., Pan, R., & Cai, J. (2020). Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers. European Journal of Operational Research, 283(2), 491-510.
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Abdi, M. R., & Labib, A. W. (2003). A design strategy for reconfigurable manufacturing systems (RMSs) using analytical hierarchical process (AHP): a case study. International Journal of production research, 41(10), 2273-2299.
Andersen, A. L., Brunoe, T. D., & Nielsen, K. (2015, September). Reconfigurable manufacturing on multiple levels: literature review and research directions. In IFIP International conference on advances in production management systems(pp. 266-273). Springer, Cham.
Andersen, A. L., Brunoe, T. D., & Nielsen, K. (2019). Engineering education in changeable and reconfigurable manufacturing: Using problem-based learning in a learning factory environment. Procedia Cirp, 81, 7-12.
Asghar, E., Baqai, A. A., & Homri, L. (2018). Optimum machine capabilities for reconfigurable manufacturing systems. The International Journal of Advanced Manufacturing Technology, 95(9), 4397-4417.
Ashraf, M., & Hasan, F. (2018). Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. The international journal of advanced manufacturing technology, 98(5), 2137-2156.
Bensmaine, A., Benyoucef, L., and Dahane, D. (2013). A non-dominated sorting genetic algorithm based approach for optimal machines selection in reconfigurable manufacturing environment. Computers & Industrial Engineering, 66(3), 519–524.
Bortolini, M., Ferrari, E., Galizia, F. G., & Regattieri, A. (2021). An optimisation model for the dynamic management of cellular reconfigurable manufacturing systems under auxiliary module availability constraints. Journal of Manufacturing Systems, 58, 442-451.
Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of manufacturing systems, 49, 93-106.
Bortolini, M., Galizia, F. G., & Mora, C. (2018). Reconfigurable manufacturing systems: Literature review and research trend. Journal of manufacturing systems, 49, 93-106.
Bortolini, M., Galizia, F. G., & Mora, C. (2019). Dynamic design and management of reconfigurable manufacturing systems. Procedia manufacturing, 33, 67-74.
Choi, Y. C., & Xirouchakis, P. (2015). A holistic production planning approach in a reconfigurable manufacturing system with energy consumption and environmental effects. International Journal of Computer Integrated Manufacturing, 28(4), 379-394.
Deif, A. M., & ElMaraghy, H. A. (2007). Assessing capacity scalability policies in RMS using system dynamics. International journal of flexible manufacturing systems, 19(3), 128-150.
Dou, J., Li, J., and Su, C. (2016). Bi objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II. The International Journal of Advanced Manufacturing Technology, 86(5-8), 1945–1962.
Gao, Guibing., Yue, Wenhui, Wang, Junshen., Ou, Wenchu. (2020). Structural-vulnerability assessment of reconfigurable manufacturing system based on universal generating function, Reliability Engineering & System Safety, 20(3): 101-107.
Haddou Benderbal, H., Dahane, M., & Benyoucef, L. (2017). Flexibility-based multi-objective approach for machines selection in reconfigurable manufacturing system (RMS) design under unavailability constraints. International Journal of Production Research, 55(20), 6033-6051.
Hashemi-Petroodi, S. E., Dolgui, A., Kovalev, S., Kovalyov, M. Y., & Thevenin, S. (2021). Workforce reconfiguration strategies in manufacturing systems: a state of the art. International Journal of Production Research, 59(22), 6721-6744.
Khan, A. S., Homri, L., Dantan, J. Y., & Siadat, A. (2020). Cost and quality assessment of a disruptive reconfigurable manufacturing system based on MOPSO metaheuristic. IFAC-PapersOnLine, 53(2), 10431-10436.
Lamy, D., Delorme, X., Lacomme, P., & Fleury, G. (2020). Toward Scheduling for Reconfigurable Manufacturing Systems. IFAC-PapersOnLine, 53(2), 10443-10448.
Lee, S., Ryu, K., & Shin, M. (2017). The development of simulation model for self-reconfigurable manufacturing system considering sustainability factors. Procedia manufacturing, 11, 1085-1092.
Li, J., Wang, A., and Tang, C. (2014). Production planning in virtual cell of reconfiguration manufacturing system using genetic algorithm. The International Journal of Advanced Manufacturing Technology, 74(1-4), 47–64.
Maganha, I., Silva, C., & Ferreira, L. M. D. (2018). Understanding reconfigurability of manufacturing systems: An empirical analysis. Journal of Manufacturing Systems, 48, 120-130.
Moghaddam, S. K., Houshmand, M., & Fatahi Valilai, O. (2018). Configuration design in scalable reconfigurable manufacturing systems (RMS); a case of single-product flow line (SPFL). International Journal of Production Research, 56(11), 3932-3954.
Ouaret, S., Kenné, J. P., & Gharbi, A. (2019). Production and replacement planning of a deteriorating remanufacturing system in a closed-loop configuration. Journal of Manufacturing Systems, 53, 234-248.
Petroodi, S. E. H., Eynaud, A. B. D., Klement, N., & Tavakkoli-Moghaddam, R. (2019). Simulation-based optimization approach with scenario-based product sequence in a reconfigurable manufacturing system (RMS): A case study. IFAC-PapersOnLine, 52(13), 2638-2643.
Singh, P. P., Madan, J., & Singh, H. (2020). A systematic approach for responsiveness assessment for product and material flow in reconfigurable manufacturing system (RMS). Materials Today: Proceedings, 28, 1643-1648.
Touzout, F. A., & Benyoucef, L. (2019). Multi-objective multi-unit process plan generation in a reconfigurable manufacturing environment: a comparative study of three hybrid metaheuristics. International Journal of Production Research, 57(24), 7520-7535.
Youssef, A. M., & ElMaraghy, H. A. (2008). Performance analysis of manufacturing systems composed of modular machines using the universal generating function. Journal of manufacturing systems, 27(2), 55-69.
Zhang, Y., Zhao, M., Zhang, Y., Pan, R., & Cai, J. (2020). Dynamic and steady-state performance analysis for multi-state repairable reconfigurable manufacturing systems with buffers. European Journal of Operational Research, 283(2), 491-510.
