Mathematical Model of Load Balancing Algorithm in Workshop Systems (Quantitative Study)

Document Type : Research Paper

Authors

1 PhD Student, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

2 Assistant Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

3 Associate Professor, Department of Industrial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran

4 Professor, Department of Industrial Management, Shahid Beheshti University, Tehran, Iran

Abstract

Balanced production line in terms of timing and load distribution on machines can improve the important indicators of timely delivery and production, which creates customer satisfaction and competitive advantage for production.Production line balance reduces lost capacity costs and unbalanced lines.Robin Hood algorithm is one of the production balance methods, the most important advantage of which is online production planning and proportional distribution of load on machines, but when the number of work orders is large, this method is not responsive.This paper focuses on optimizing and developing the mathematical model of Robin Hood algorithm so that this model can plan a large number of work orders.Development of the model with two objectives: minimizing the maximum load on the production system and minimizing the order completion time Takes place on machines.The problem of production balance is one of the NP-hard problems, and since no Iuick and feasible solution to such problems has been found in a reasonable time, the NSGA algorithm can be found to find close to optimal solutions to the proposed multi-objective mathematical model.-II has been used.The results of the development of the mathematical model in Robin Hood algorithm show that the balance of the production line in the large number of orders and momentary changes in the production plan, using this method can improve production planning.

Keywords


[1]    Baudin, M. (2002). “Lean assembly: the nuts and bolts of making assembly operations flow”. New York, USA: Productivity Press.
[2]    Levi, D.S., Kaminsky, P., Levi, E.S. (2003). “Designing and managing the supply chain: Concepts, strategies, and case studies”. McGraw-Hill.
[3]    Razif, M., Make, A., Fadzil, M., Ab, F., Make, M.R.A., Rashid, M.F.F.A., & Razali, M.M. (2017). “A review of two-sided assembly line balancing problem”. The International Journal of Advanced Manufacturing Technology, 89: 1743-1763.
[4]    Sikora, C., Gustavo, S., Lopes Thiago, C., Schibelbain, M. (2017). “Integer based formulation for the simple assembly line balancing problem with multiple identical tasks”. Computers & Industrial Engineering.104: 134–144.
[5]    Boysen, N., Fliedner, M., Scholl, A.A. (2007). “classification of assembly line balancing problems”. European Journal of Operational Research, 183:674-673.
[6]    Yano, C.A., Bolat, A. (1989). “Survey, development, and application of algorithms for seIuencing paced assembly”.
[7]    Bard, J. F, (1989) “Assembly line balancing with parallel workstations and dead time”. International Journal of Production Research, 27:1005–1018.
[8]    Scholl, A., Klein, R., Domschke, W. (1998). “Pattern based vocabulary building for effectively seIuencing mixed model assembly lines”. Journal of Heuristics, 4:359–381.
[9]    Paksoy, T., Özceylan, E. Gökçen, H. (2012). “Supply chain optimisation with assembly line balancing”. International Journal of Production Research, 50: 3115-3136.
[10] Bautista, J., Pereira, J. (2009). “A dynamic programming-based heuristic for the assembly line balancing problem”. European Journal of Operational Research, 194 (3): 787-794.
[11] Thangavelu, S.R., Shetty, C.M. (1971) “Assembly line balancing by zero-one integer programming”. AIIE Transactions, 3 (1): 61-68.
[12] Eghtesadifard, M., Khalifeh, M., Khorram, M. (2020). “A systematic review of research themes and hot topics in assembly line balancing through the web of science within 1990–2017” Computers & In dustrialEngineering,139.
[13] Kucukkoc, I., Zhang, D. (2014). “Mathematical model and agent-based solution approach for the simultaneous balancing and seIuencing of mixed-model parallel two-sided ssembly lines”. International Journal of Production Economics, 158: 314-333.
[14] Caramia. M, Dell’Olmo.P.(2006). “Effective Resource Management in Manufacturing Systems Optimization Algorithms for Production Planning”. Springer series in advanced manufacturing.
[15] Pereira, J., Álvarez-Miranda, E. (2017). “An exact approach for the robust assembly line balancing problem”. Omega.78: 85-98.
[16] Rescenzi, P., Gambosi, G. (2007). “On-line load balancing made simple: Greedy strikes back”. Journal of Discrete Algorithms.5 (1): 162-175.
[17] Hou, L., Kang, L. (2011). “Online and semi-online hierarchical schedul ing for load balancing on uniform machines”. Theoretical Computer Science.42 (12-14): 1092-1098.
[18] Lou, T., Xu, Y. (2015). “Semi-online hierarchical load balancing problem with bounded processing times”. Theoretical Computer Science. 607 (1): 75-82.
[19] Rahmani, N., Najafi, A. (2020). “Online Distribution and Load Balancing Optimization Using the Robin Hood and Johnson Hybrid Algorithm”. Journal of Optimization in Industrial Engineering.13 (2): 17-26.
[20] Fisel, J., Exner, Y., Stricker, N., Lanza, G. (2019) “Changeability and flexibility of assembly line balancing as a multi-objective optimization problem”. Journal of Manufacturing Systems, 53: 150-158.
[21] متقی، هایده.(1398)."مدیریت تولید و عملیات". انتشارات آوای شروین، تهران: ویرایش پنجم.
[22] Bauer, A., Bowden, R., Browne, J., Duggan, J., Lyons, G (1991) Shop Floor Control Systems, From design to implementation, Chapman and Hall, London.
[23] جعفر زنجانی، حامد زندیه، مصطفی خلیل زاده، محمد. (1399)."مدل برنامه‌ریزی تصادفی و رویکرد حل تجزیه بندرز برای برنامه‌ریزی یکپارچه تولید و نگهداری ‌تعمیرات در سیستم تولید چندکارخانه‌ای". نشریه پژوهش‌های مهندسی صنایع در سیستم‌های تولید. 8(16): 77-93.
[24] Wei, J., Xu, D., Iin Y., Huang, R. (2017) “ON-LINE LOAD BALANCING WITH TASK BUFFER”. Computing and Informatics, 36: 1207-1234.
[25] Graham, R.L. (1996). “Bounds for certain multiprocessing anomalies”. Bell Syst Tech.J., 45: 1563–1581.
[26] شهسواری‌پور، ناصر، کاظمی، مجتبی، اسدی، حامد، حیدری، عظیم (1395). "برنامه‌ریزی و زمان‌بندی تولید با رویکرد الگوریتم‌های فراابتکاری " .انتشارات کیان رایانه.
[27] Garey, M.R., Johnson, D.S. Sethi, R. (1976). “The complexity of flow shop and job shop scheduling”. Mathematics of Operations Research, 1(2):117-129.
[28] سمویی، پروانه، فتاحی، پرویز. (1396). "مقایسه و تحلیلی بر استفاده از الگوریتم‌های فراابتکاری برای حل مسائل زمان‌بندی تولید کارگاهی". مجله تحقیق در عملیات در کاربردهای آن.14(1): 63-76.
[29] Coello, Carlos., Gary, BL., V D.A. (2007). “Evolutionary Algorithms for Solving Multi-Objective Problems”, Publisher Springer US, eBook ISBN 978-0-387-36797-2
DOI 10.1007.978-0-387-36797-2.
[30] Deb, K., Pratap, A., Agarwal, S., Meyarivan, T. (2002) “A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II”. IEEE Trans on evolutionary computation6(N2): 182-19.
[31] Ciro, G.C., Dugardin, F., Yalaoui, F., Kelly, R. (2016). “A NSGA-II and NSGA-III comparison for solving an open shop scheduling problem with resource constraints”, IFAC-Papers Online.49(12): 1272-1277.
[32] Taguchi, G. (1986). Introduction to Iuality engineering: designing Iuality into products and processes.
[33] Hamta, N., Fatemi Ghomi, S.M.T., Jolai, F., Bahalke, U. (2011). “Bi-criteria assembly line balancing by considering flexible operation times”, Applied Mathematical Modelling.35: 5592-5606.
Hamta, N., Fatemi Ghomi, S.M.T., Jolai, F., Akbarpour, M. (2013). “A hybrid PSO algorithm for a multi-objective assembly line balancing problem with flexible operation times, seIuence-dependent setup times and learning effect”, Int. J. Production Economics.141: 99-111.