A Mixed Integer Mathematical Model for Loading Problem in Seru Manufacturing Systems and Matheuristic Solution Approach

Öz

With the technological and economic progress, the disappearance of the boundaries has led to the continuous change in the content and volume of customer demands therefore it has revealed the necessity of the companies to respond to these changes rapidly. This necessity has also led companies to modern production systems that will meet the speed of mass production systems and the resilience capability of flexible production systems. One of these production systems is the seru production systems which started to be used in the electronics sector in Japan from the 90s. These systems divide traditional assembly lines into cells called seru, where all tasks in the production of goods are performed by one or more highly skilled employees. The aim of this study is to obtain optimal seru loading plans for seru production system that meets the speed of mass production and the product diversity capability of flexible production systems. Therefore, Seru Loading Problem, which is one of the problems encountered in seru production systems, is handled. A new mixed integer mathematical model and mat-heuristic algorithm are proposed to obtain the optimal worker and workload plan for a particular seru design. The efficiency of the results obtained from the model and the heuristic method has been shown in comparison with a real application data set obtained from the literature and it has been observed that the obtained seru loading plans are effective as a result of the comparison.

Anahtar Kelimeler

• Seru production system
• seru loading problem
• matheuristics

Seru üretim sistemlerinde yükleme problemi için karışık tamsayılı bir matematiksel model ve mat-sezgisel çözüm yaklaşımı

Öz

Teknolojik ve ekonomik ilerlemeyle birlikte sınırların ortadan kalkması, müşteri taleplerinin içerikleri ve hacimlerinin sürekli bir şekilde değişmesini ve beraberinde işletmelerin bu değişimlere hızlı cevap verebilmesi gerekliliğini ortaya çıkarmıştır. Bu ihtiyaç, işletmeleri kitlesel üretim sistemlerinin hızını ve esnek üretim sistemlerinin de esneklik kabiliyetini birlikte karşılayacak modern üretim sistemlerine yöneltmiştir. Bu üretim sistemlerinden bir tanesi de Japonya’da 90’lı yıllarda elektronik sektöründe kullanılmaya başlanan seru üretim sistemleridir. Bu sistemler, geleneksel montaj hatlarını seru adı verilen hücrelere bölerek, bir ürünün üretimindeki tüm görevlerin tek ya da birden fazla yüksek yetenekli çalışan tarafından gerçekleştirildiği sistemlerdir. Bu çalışmanın amacı, kitle üretimin hızını ve esnek üretimin ürün çeşitliliği sağlama kapasitesini karşılayan seru üretim sistemi için optimum seru yükleme planları elde etmektir. Bu nedenle, seru üretim sistemlerinde karşılaşılan problemlerden olan seru yükleme problemi ele alınmıştır. Belirli bir seru tasarımına ait optimal işçi ve iş yükleme planını elde etmeye yönelik karışık tamsayılı yeni bir matematiksel model ve bir mat-sezgisel algoritma önerilmiştir. Modelden ve sezgisel yöntemden elde edilen sonuçların etkinliği, literatürden alınan gerçek bir uygulama veri setiyle karşılaştırmalı olarak gösterilmiş karşılaştırma sonucunda elde edilen seru yükleme planlarının etkin olduğu gözlemlenmiştir.

Anahtar Kelimeler

• Seru üretim sistemi
• seru yükleme problemi
• mat-sezgisel yöntem

Kaynakça

1. Stecke K. E., Yin Y. ve Kaku I., Seru Production: An Extension of Just-in-Time Approach for Volatile Business Environments, Analytical Approaches to Strategic Decision-Making: Interdisciplinary Considerations, Editör: Tavana M., IGI Global, A.B.D., 45-58, 2014.
2. Liu C. G., Stecke K. E., Lian J. ve Yin Y., An implementation framework for seru production, Intl. Trans. in Op. Res., 21, 1-19, 2014.
3. Liu C. G., Li W., Lian J. ve Yin Y., Reconfiguration of assembly systems: From conveyor assembly line to serus, J. Manuf. Syst., 31, 312-325, 2012.
4. Kaku I., Is Seru a Sustainable Manufacturing System?, Procedia Manuf., 8, 723-730, 2017.
5. Yin Y., Stecke K. E. ve Li D., The evolution of production systems from Industry 2.0 through Industry 4.0, Int. J. Prod. Res., 56(1-2), 848-861, 2018.
6. Kaku I., Murase Y. ve Yin Y., A study on human-task-related performances in converting conveyor assembly line to cellular manufacturing, European J. Industrial Engineering, 2(1), 17-34, 2008.
7. Kaku I., Gong J., Tang J. ve Yin Y., Modeling and numerical analysis of line-cell conversion problems, Int. J. Prod. Res., 47(8), 2055–2078, 2009.
8. Liu C. G., Yang N., Li W. J., Lian J., Evans S. ve Yin Y., Training and assignment of multi-skilled workers for implementing seru production systems, Int. J. Adv. Manuf. Technol., 69, 937–959, 2013.

9. Yu Y., Tang J., Gong J., Yin Y. ve Kaku I., Mathematical analysis and solutions for multi-objective line-cell conversion problem, Eur. J. Oper. Res., 236, 774–786, 2014.
10. Liu C. G., Dang F., Li W. J., Lian J., Evans S. ve Yin Y., Production planning of multi-stage multi-option seru production systems with sustainable measures, J. Cleaner Prod., 105, 285-299, 2015.
11. Manupati V. K., Deepthi T. V., Ramakotaiah K. ve Rao S. S., Reconfiguration of Networked Seru Production systems in an Indian Perspective, Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management, Dubai, B.A.E., 3-5 Mart 2015.
12. Luo L., Zhang Z. ve Yin Y., Seru Loading with Worker-Operation Assignment in Single Period, 2016 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Bali, Indonesia, 1055-1058, 2016.
13. Yu Y., Wang S., Tang J., Kaku I. ve Sun W., Complexity of line‑seru conversion for different scheduling rules and two improved exact algorithms for the multi‑objective optimization, SpringerPlus, 5(809), 1-26, 2016.
14. Li X., Li D., Wu X. ve Zheng H., A Cooperative Co-evolution Approach for a Line-seru conversion problem, 2017 IEEE Congress on Evolutionary Computation (CEC), San Sebastian, Spain, 1406-1411, 2017.
15. Luo L. ve Zhang Z., Modelling and numerical analysis of seru loading problem under uncertainty, European J. Industrial Engineering, 11(2), 185-204, 2017.
16. Ying K. C. ve Tsai Y. J., Minimising total cost for training and assigning multiskilled workers in seru production systems, Int. J. Prod. Res., 55(10), 2978–2989, 2017.
17. Yu Y., Sun W., Tang J. ve Wang J., Line-hybrid seru system conversion: Models, complexities, properties, solutions and insights, Comput. Ind. Eng., 103, 282–299, 2017.
18. Yu Y., Sun W., Tang J., Kaku I. ve Wang J., Line-seru conversion towards reducing worker(s) without increasing makespan: models, exact and meta-heuristic solutions, Int. J. Prod. Res., 55(10), 2990–3007, 2017.
19. Lian J., Liu C. G., Li W. J. ve Yin Y., A multi-skilled worker assignment problem in seru production systems considering the worker heterogeneity, Comput. Ind. Eng., 118, 366–382, 2018.
20. Wang Y. ve Tang J., Cost And Service-Level-Based Model For A Seru Production System Formation Problem With Uncertain Demand, J. Syst. Sci. Syst. Eng., 27(4), 519–537, 2018.
21. Wu L., Chan F. T. S., Niu B. ve Li L., Cross-trained worker assignment and comparative analysis on throughput of divisional and rotating seru, Industrial Management & Data Systems, 18(5), 1114-1136, 2018.
22. Yu Y., Wang J., Ma K. ve Sun W., Seru system balancing: Definition, formulation, and exact solution, Comput. Ind. Eng., 122, 318–325, 2018.
23. Ren, H. ve Wang, D., Analysis of the Effect of the Line-Seru Conversion on the Waiting Time with Batch Arrival, Math. Prob. Eng., 2019, 4036794, 2019.
24. Sun, W., Wu, Y., Lou, Q. ve Yu, Y., A Cooperative Coevolution Algorithm for the Seru Production with Minimizing Makespan, IEEE Access, 7, 5662-5670, 2019.
25. Ayough, A., Hosseinzadeh, M. ve Motameni, A., Job rotation scheduling in the Seru system: shake enforced invasive weed optimization approach, Assembly Automation, 40(3), 461-474, 2020.
26. Yılmaz, Ö. F., Operational strategies for seru production system: a bi-objective optimisation model and solution methods, Int. J. Prod. Res., 58(11), 3195-3219, 2020.
27. Yılmaz, Ö. F., Attaining flexibility in seru production system by means of Shojinka: An optimization model and solution approaches, Computers and Operations Research, 119, 104917, 2020.
28. Croce F. D., Grosso A. ve Salassa F., Matheuristics: Embedding MILP solvers into heuristic algorithms for combinatorial optimization problems, Heuristics: Theory and applications, Editör: Siarry P., Nova Science Publishera, Inc., New York, 53–68, 2013.
29. Küçükaydın H., Column generation based matheuristics for a vehicle routing problem with time windows and variable start time, Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4), 2061 – 2078, 2019.