Sanal Hücresel Üretim Sistemi İçin Üretim Kontrol Sisteminin Belirlenmesi

Yıl 2018, Cilt: 6 Sayı: 3, 644 - 658, 30.09.2018

Ömer Faruk Yılmaz

https://doi.org/10.29109/gujsc.397467

Öz

Sanal
hücresel üretim sistemi (SHÜS) yeni ürün kabulüne izin vermekle birlikte
talepteki değişkenliğe karşılık verebilmektedir. Böylece, üretim ortamında
herhangi bir değişikliğe gerek duyulmadan üretim gerçekleştiren firmalara
avantajlar sunmaktadır. SHÜS için uygun üretim kontrol sisteminin belirlenmesi
oldukça önemli bir konudur ve bu çalışma içerisinde bu probleme
odaklanılmıştır. SHÜS için en uygun alternatifin seçilmesi için dört farklı
alternatif aralık değerli sezgisel bulanık analitik hiyerarşi prosesi (ADSBAHP)
metoduyla beş farklı kriter ışığında değerlendirilmiştir. Sonuçlar SHÜS için
CONWIP üretim kontrol sistemi alternatifinin diğer alternatiflere üstünlük
sağladığını göstermektedir.

Anahtar Kelimeler

Üretim kontrol sistemleri, Aralık değerli sezgisel bulanık analitik hiyerarşi prosesi, Sanal hücresel üretim sistemi

Kaynakça

• [1] Rajesh, K. D., Krishna, M. M., Ali, M. A., & Chalapathi, P. V. (2017). A Modified Hybrid Similarity Coefficient Based Method for Solving the Cell Formation Problem in Cellular Manufacturing System. Materials Today: Proceedings, 4(2), 1469-1477.[2] Venkataramanaiah, S. (2008). Scheduling in cellular manufacturing systems: an heuristic approach. International Journal of Production Research, 46(2), 429-449.[3] Yilmaz, O. F., Cevikcan, E., & Durmusoglu, M. B. (2016). Scheduling batches in multi hybrid cell manufacturing system considering worker resources: A case study from pipeline industry. Advances in Production Engineering & Management, 11(3), 192.[4] Norman, B. A., Tharmmaphornphilas, W., Needy, K. L., Bidanda, B., & Warner, R. C. (2002). Worker assignment in cellular manufacturing considering technical and human skills. International Journal of Production Research, 40(6), 1479-1492.[5] Aglan, C., & Durmusoglu, M. B. (2015). Lot-splitting approach of a hybrid manufacturing system under CONWIP production control: a mathematical model. International Journal of Production Research, 53(5), 1561-1583.[6] Dakov, I., Lefterova, T., & Petkova, A. (2010). Layout and Production Planning of Virtual Cellular Manufacturing Systems for Mechanical Machining. The Journal of Economic Asymmetries, 7(1), 43-67.[7] Kesen, S. E. (2010). Sanal İmalat Hücrelerinde İş Çizelgelenmesi İçin Yeni Çözüm Yaklaşımları, Doktora Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.[8] Mahdavi, I., Aalaei, A., Paydar, M. M., & Solimanpur, M. (2009, July). Production planning and cell formation in dynamic virtual cellular manufacturing systems with worker flexibility. In Computers & Industrial Engineering, 2009. CIE 2009. International Conference on (pp. 663-667). IEEE.[9] Nomden, G., & van der Zee, D. J. (2008). Virtual cellular manufacturing: Configuring routing flexibility. International Journal of Production Economics, 112(1), 439-451.[10] Grosfeld-Nir, A., Magazine, M., & Vanberkel, A. (2000). Push and pull strategies for controlling multistage production systems. International Journal of Production Research, 38(11), 2361-2375.[11] Mertins, K., & Lewandrowski, U. (1999). Inventory safety stocks of Kanban control systems. Production Planning & Control, 10(6), 520-529.[12] Spearman, M. L., Woodruff, D. L., & Hopp, W. J. (1990). CONWIP: a pull alternative to kanban. The International Journal of Production Research, 28(5), 879-894.[13] Cevikcan, E., & Durmusoglu, M. B. (2014). An integrated job release and scheduling approach on parallel machines: An application in electric wire-harness industry. Computers & Industrial Engineering, 76, 318-332.[14] Spearman, M. L., & Zazanis, M. A. (1992). Push and pull production systems: issues and comparisons. Operations research, 40(3), 521-532.[15] Steele, D. C., & Malhotra, M. K. (1997). Factors affecting performance of period batch control systems incellular manufacturing. International journal of production research, 35(2), 421-446.[16] Li, J. W., & Barnes, D. J. (2000). Investigating the factors influencing the shop performance in a job shop environment with kanban-based production control. International Journal of Production Research, 38(18), 4683-4699.[17] Ryan, S. M., & Fred Choobineh, F. (2003). Total WIP and WIP mix for a CONWIP controlled job shop. IIE Transactions, 35(5), 405-418.[18] Islam, N., & Mohamed, P. S. (2003). Coexistence of JIT and MRP in a manufacturing cell. International Journal of Modelling and Simulation, 23(4), 251-257.[19] Sharma, S., & Agrawal, N. (2009). Selection of a pull production control policy under different demand situations for a manufacturing system by AHP-algorithm. Computers & Operations Research, 36(5), 1622-1632.[20] Li, J. W. (2010). Simulation study of coordinating layout change and quality improvement for adapting job shop manufacturing to CONWIP control. International Journal of Production Research, 48(3), 879-900.[21] Moreira, M. D. R. A., & Alves, R. A. F. (2012). Input-output control order release mechanism in a job-shop: how workload control improves manufacturing operations. International Journal of Computational Science and Engineering, 7(3), 214-223.[22] Gong, Q., Yang, Y., & Wang, S. (2014). Information and decision-making delays in MRP, KANBAN, and CONWIP. International Journal of Production Economics, 156, 208-213.[23] Müller, E., Tolujew, J., & Kienzle, F. (2014). Push-Kanban–a kanban-based production control concept for job shops. Production Planning & Control, 25(5), 401-413.[24] Romagnoli, G. (2015). Design and simulation of CONWIP in the complex flexible job shop of a Make-To-Order manufacturing firm. International Journal of Industrial Engineering Computations, 6(1), 117-134.[25] Thürer, M., Land, M. J., Stevenson, M., & Fredendall, L. D. (2016). Card-based delivery date promising in high-variety manufacturing with order release control. International Journal of Production Economics, 172, 19-30.[26] Konefal, J. G. (2017). Applying factory physics to manual assembly at an aerospace fabrication site (Doctoral dissertation, Massachusetts Institute of Technology).[27] Atanassov, K. T. (1989). More on intuitionistic fuzzy sets. Fuzzy sets and systems, 33(1), 37-45.[28] Xu, Z. S., & Jian, C. H. E. N. (2007). Approach to group decision making based on interval-valued intuitionistic judgment matrices. Systems Engineering-Theory & Practice, 27(4), 126-133.[29] Xu, Z. (2010). A method based on distance measure for interval-valued intuitionistic fuzzy group decision making. Information sciences, 180(1), 181-190.[30] Chen, T. Y., Wang, H. P., & Lu, Y. Y. (2011). A multicriteria group decision-making approach based on interval-valued intuitionistic fuzzy sets: A comparative perspective. Expert Systems with Applications, 38(6), 7647-7658.[31] Chen, Z., & Yang, W. (2012). A new multiple criteria decision making method based on intuitionistic fuzzy information. Expert Systems with Applications, 39(4), 4328-4334.[32] ZHANG, Y. J., Pei-Jun, M. A., Xiao-Hong, S. U., & ZHANG, C. P. (2012). Multi-attribute group decision making under interval-valued intuitionistic fuzzy environment. Acta Automatica Sinica, 38(2), 220-227.[33] Jin, F., Pei, L., Chen, H., & Zhou, L. (2014). Interval-valued intuitionistic fuzzy continuous weighted entropy and its application to multi-criteria fuzzy group decision making. Knowledge-Based Systems, 59, 132-141.[34] Tong, X., & Yu, L. (2015). A novel MADM approach based on fuzzy cross entropy with interval-valued intuitionistic fuzzy sets. Mathematical Problems in Engineering, 2015.[35] Onar, S. C., Oztaysi, B., Otay, İ., & Kahraman, C. (2015). Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets.Energy, 90, 274-285.[36] Kahraman, C., Cevik Onar, S., & Oztaysi, B. (2016). A comparison of wind energy investment alternatives using interval-valued intuitionistic fuzzy benefit/cost analysis. Sustainability, 8(2), 118.[37] Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E. K., Cevik Onar, S., Yazdani, M., & Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25(1), 1-12.[38] Yılmaz, Ö. F., & Durmuşoğlu, M. B. (2018). An Integrated Methodology for Order Release and Scheduling in Hybrid Manufacturing Systems: Considering Worker Assignment and Utility Workers. In Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems (pp. 125-161). IGI Global.