نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی صنایع، دانشکده فنی‌و‌مهندسی، دانشگاه بوعلی سینا، همدان، ایران.

10.22105/dmor.2021.265665.1294

چکیده

هدف: یکی از موضوعات امروزه تولیدکنندگان، بحث تنوع سلیقههای مشتریان است که برای مدیریت این شرایط با کمترین تغییر در محصولات تولیدی، نیاز به خطوط چندگانه است که انعطاف لازم برای تولید این محصولات را دارا باشند. از سویی خیلی از محصولات نیازمند عملیات مونتاژند. هدف اصلی مقاله بالانس این دسته از مسائل با توجه به شرایط نیروی کار و محصولات مختلف است.
روش‌شناسی پژوهش: در این مقاله با ارائه مدل‌های ریاضی به حداقل کردن تعداد ایستگاه‌های کاری به ازای یک‌زمان سیکل معین پرداخته می‌شود. در مدل اول تمام پارامترها قطعی است. ازآنجاکه ممکن است تقاضای مشتریان ثابت نباشد و این عامل می‌تواند روی زمان سیکل اثرگذار باشد در مدل دوم، از رویکرد استوار برای این مسئله استفاده می‌شود.
یافته‌ها: تجزیه‌وتحلیل روی مسائل مختلف نشان می‌دهد که رویکرد مدل‌سازی استوار طراحی مطمئن‌تری ارائه می‌دهد و به تصمیم‌گیرندگان اجازه می‌دهد تا مونتاژ بهتری بر اساس درک بهتر از شرایط کوتاه‌مدت و بلندمدت تحت شرایط عدم قطعیت تقاضا داشته باشند.
اصالت/ارزش‌افزوده علمی: در این مقاله دو مدل ریاضی جدید برای بالانس خط مونتاژ مدل‌های چندگانه ارائه می‌گردد که در آن عملیات مونتاژ توسط کارگران و به شکل دستی صورت می‌پذیرد و برای برنامه‌ریزی دقیق‌تر، تفاوت‌هایی که کارگران از منظر اثر یادگیری و فراموشی دارند، بر روی بالانس خط مونتاژ منظور گردید.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Multi-model Assembly Line Balancing and Sequencing with Operators’ Learning and Forgetting Effects under Uncertainty

نویسندگان [English]

  • Ahmad Faridanifar
  • Parvaneh Samouei

Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamadan, Iran.

چکیده [English]

Purpose: One of the topics for manufacturers today is to discuss the diversity of customer tastes, which to manage this situation with the least change in products, requires multiple lines that have the necessary flexibility to produce these products. On the other hand, many products require assembly operations. The main purpose of this article is to balance these issues according to the conditions of the workforce and different products.
Methodology: This paper presents two mathematical models to minimize the number of workstations per given cycle time. In the first model, all parameters are definite. Since customer demand may not be constant and this factor can affect the cycle time, the second model uses a robust approach to this issue.
Findings: Analysis of various issues shows that a robust modeling approach provides a more reliable design and allows decision makers to have better assembly based on a better understanding of short-term and long-term conditions under conditions of demand uncertainty.
Originality/Value: In this paper, two new mathematical models for assembly line balance are presented. Multi-models in which assembly operations are performed manually by workers and for more accurate planning, the differences that workers have in terms of learning and forgetfulness effect on assembly line balance are considered.

کلیدواژه‌ها [English]

  • Assembly Line Balancing and Sequencing
  • Multi-model
  • Learning and Forgetting Effects
Arditi, D., Tokdemir, O. B., & Suh, K. (2001). Effect of learning on line-of-balance scheduling. International journal of project management19(5), 265-277.
Azizi, N., Zolfaghari, S., & Liang, M. (2010). Modeling job rotation in manufacturing systems: the study of employee's boredom and skill variations. International journal of production economics123(1), 69-85.
Battaïa, O., & Dolgui, A. (2013). A taxonomy of line balancing problems and their solutionapproaches. International journal of production economics142(2), 259-277.
Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming88(3), 411-424.
Boysen, N., Fliedner, M., & Scholl, A. (2008). Assembly line balancing: which model to use when?. International journal of production economics111(2), 509-528.
Chiang, W. C., Kouvelis, P., & Urban, T. L. (2007). Line balancing in a just-in-time production environment: balancing multiple U-lines. IIE transactions39(4), 347-359.
Cochran, E. B. (1973). The dynamics of work standards. Manufacturing engineering and management, 70(2), 28-31.
Fakhrzad, M., & Alinezhad, E. (2013). Advanced planning and scheduling with a learning effect in the flexible job shop manufacturing system. Journal of industrial engineering research in production systems, 1(1), 13-24. (In Persian). https://ier.basu.ac.ir/article_493_en.html
Fattahi, P., Samouei, P., & Zandiyeh, M. (2017). A Multi-objective simulated annealing for simultaneous two-sided assembly line balancing and operators’ assignment. Journal of production and operations management, 8(1), 1-20. (In Persian). DOI: 10.22108/JPOM.2017.21543
Globerson, S., Levin, N., & Shtub, A. (1989). The impact of breaks on forgetting when performing a repetitive task. IIE transactions21(4), 376-381.
Hazır, Ö., & Dolgui, A. (2013). Assembly line balancing under uncertainty: robust optimization models and exact solution method. Computers & industrial engineering65(2), 261-267.
Hoedt, S., Claeys, A., Schamp, M., Van De Ginste, L., Aghezzaf, E. H., & Cottyn, J. (2019). The effect of job similarity on forgetting in multi-task production. Procedia manufacturing39, 983-990.
Hoedt, S., Claeys, A., Schamp, M., Van Landeghem, H., & Cottyn, J. (2018). Countering the forgetting effect in mixed-model manual assembly. IFAC-papersonline51(11), 856-861.
Hosseinzade, A., & Mottaghi, H. (2014). Production and operations management. Avae Shirin Publications. (In Persian).
Jaber, M. Y. (2016). The lot sizing problem and the learning curve: a review. Learning curves, 283-310.
Jaber, M. Y., & Guiffrida, A. L. (2004). Learning curves for processes generating defects requiring reworks. European journal of operational research159(3), 663-672.
Jafari Asl, A., Solimanpur, M., & Shankar, R. (2019). Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry. OPSEARCH56(3), 603-627.
Kher, H. V., Malhotra, M. K., Philipoom, P. R., & Fry, T. D. (1999). Modeling simultaneous worker learning and forgetting in dual resource constrained systems. European journal of operational research115(1), 158-172.
Kumar, A., Pattanaik, L. N., & Agrawal, R. (2019). Optimal sequence planning for multi-model reconfigurable assembly systems. The international journal of advanced manufacturing technology100(5-8), 1719-1730.
Lolli, F., Balugani, E., Gamberini, R., & Rimini, B. (2017). Stochastic assembly line balancing with learning effects. IFAC-papersonline50(1), 5706-5711.
Monden, Y. (1994). Toyota production system. Springer US.
Musavi, F., Saremi, M., & Alibabaei, A. (2016). The effect of assembly line redesign based on engineering techniques on productivity and ergonomics factors. Iran occupational health12(6), 1-15. (In Persian).  http://ioh.iums.ac.ir/article-1-1508-en.html
Pereira, J., & Álvarez-Miranda, E. (2018). An exact approach for the robust assembly line balancing problem. Omega78, 85-98.
Sadeghigivi, Z. (2014). The effect of learning, forgetting, fatigue, and recovery on the performance of dual-resource constrained (DRC) systems (PhD Desseration, Ryerson University). Retrived from https://digital.library.ryerson.ca/islandora/object/RULA%3A3477
Salveson, M. E. (1955). The assembly line balancing problem. Journal of industrial engineering, 6, 62-69.
Samouei, P., & Ashayeri, J. (2019). Developing optimization & robust models for a mixed-model assembly line balancing problem with semi-automated operations. Applied mathematical modelling72, 259-275.
Thangavelu, S. R., & Shetty, C. M. (1971). Assembly line balancing by zero-one integer programming. AIIE transactions3(1), 61-68.
Thomopoulos, N. T. (2014). Assembly line planning and control. Springer International Publishing.
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of the aeronautical sciences3(4), 122-128.
Xu, W., & Xiao, T. (2011). Strategic robust mixed model assembly line balancing based on scenario planning. Tsinghua science and technology16(3), 308-314.
Yeh, D. H., & Kao, H. H. (2009). A new bidirectional heuristic for the assembly line balancing problem. Computers & industrial engineering57(4), 1155-1160.
Yu, H., & Shi, W. (2013, July). A genetic algorithm for multi-model assembly line balancing problem. 2013 IEEE international symposium on assembly and manufacturing (ISAM) (pp. 369-371). IEEE.
Zhang, B., Xu, L., & Zhang, J. (2021). Balancing and sequencing problem of mixed-model U-shaped robotic assembly line: mathematical model and dragonfly algorithm based approach. Applied soft computing98, 106739. https://doi.org/10.1016/j.asoc.2020.106739