Özlem Uzun ARAZ, Ceyhun ARAZ, Özgür ESKİ

Dinamik üretim sistemleri için kanban sayısının belirlenmesi: Bütünleşik bir yöntem

Tam zamanında üretim sistemleri (TZÜ), işletmelerin doğru zamanda, müşterinin istediği miktarda üretim yapmalarına olanak sağlayan, böylelikle stoklarını azaltmaya teşvik eden bir yönetim felsefesidir. TZÜ felsefesinin en önemli parçası, malzeme hareketlerini gerçekleştirmek için kullanılan kanban sistemleridir. Kanban sistemlerinde, iş istasyonlarında kullanılacak kanban sayılarının belirlenmesi en temel problem olarak karşımıza çıkmaktadır. Kullanılacak kanban sayıları üretim sisteminin performansı üzerinde etkilidir. Bu çalışmanın temel amacı, Kanban sistemlerinde, kart sayılarının dinamik belirlenebilmesi için kullanılabilecek bir yöntem geliştirmektir. Önerilen yöntemin temelinde, üretim sisteminin anlık veri alınarak izlenmesi ve sistem durum değişkenlerinde meydana gelen farklılıkların dikkate alınarak Kanban sayılarının yeniden düzenlemesi yatmaktadır. Bu amaçla yapılan çalışmada benzetim, yapay sinir ağları ve Mamdani tipi bulanık çıkarsama sistemleri entegre edilerek bütünleşik bir dinamik kanban sayıları belirleme yöntemi geliştirilmiştir. Önerilen yöntem, benzetim ortamımda modellenen hipotetik bir üretim sistemine uygulanmıştır. Elde edilen sonuçlar, önerilen yöntemin verimliliğini ve etkinliğini göstermiştir.

Anahtar Kelimeler:

Kanban üretim sistemleri, Benzetim, Yapay sinir ağları, Bulanık çıkarsama sistemleri

Determining the number of kanbans for dynamic production systems: An integrated methodology

Just-in-time (JIT) is a management philosophy that reduces the inventory levels and eliminates manufacturing wastes by producing only the right quantity at the right time. A kanban system is one of the key elements of JIT philosophy. Kanbans are used to authorize production and to control movement of materials in JIT systems. In Kanban systems, the efficiency of the manufacturing system depends on several factors such as number of kanbans, container size etc. Hence, determining the number of kanbans is a critical decision in Kanban systems. The aim of this study is to develop a methodology that can be used in order to determine the number of kanbans in a dynamic production environment. In this methodology, the changes in system state is monitored in real time manner, and the number of the kanbans are dynamically re-arranged. The proposed methodology integrates simulation, neural networks and Mamdani type fuzzy inference system. The methodology is modelled in simulation environment and applied on a hypothetic production system. We also performed several comparisons for different control policies to show the effectiveness of the proposed methodology.

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Graves RJ, Konopka JM, Milne RJ. “Literature review of material flow control mechanisms”. Production Planning & Control, 6(5), 395-403, 1995.
Gupta SM, Al-Turki YAY. “An algorithm to dynamically adjust the number of kanbans in stochastic processing times and variable demand environment”. Production Planning & Control, 8(2), 133-141, 1997.
Belisario RS, Pierreval H. “Using genetic programming and simulation to learn how to dynamically adapt the number of cards in reactive pull systems”. Expert Systems with Applications, 42(6), 3129-3141, 2015.
Araz OU, Eski O, Araz C. “Determining the parameters of dual-card kanban system: an integrated multicriteria and artificial neural network methodology”. The International Journal of Advanced Manufacturing Technology, 38(9), 965-977, 2008.
Guneri AF, Kuzu A, Taskin Gumus A. “Flexible kanbans to enhance volume flexibility in a JIT environment: A simulation based comparison via ANNs”. International Journal of Production Research, 47(24), 6807-6819, 2009.
Akturk MS, Erhun F. “An overview of design and operational issues of kanban systems”. International Journal of Production Research, 37(17), 3859-3881, 1999.
Gupta YP, Gupta MC. “A system dynamics model for a multi-stage, multi-line dual-card JIT-Kanban system”. International Journal of Production Research, 27(2), 309-352. 1989.
Karmarkar US, Kekre S. “Batching Policy in Kanban Systems”. Journal of Manufacturing Systems, 8(4), 317-328, 1989.
Philipoo PR, Ree LP, Taylo BW. “Simultaneously determining the number of kanbans, container sizes, and the final-assembly sequence of products in a just-in-time shop”. International Journal of Production Research, 34(1), 51-69, 1996.
Moeeni F, Sanchez SM, Vakha Ria AJ. “A robust design methodology for Kanban system design”. International Journal of Production Research, 35(10), 2821-2838, 1997.
Kochel P, Nielander U. “Kanban optimization by simulation and evolution”. Production Planning Control, 13(8), 725-734. 2002.
Shahabudeen P, Krishnaiah K, Thulasi Narayanan M. “Design of a two-card dynamic kanban system using a simulated annealing algorithm”. The International Journal of Advanced Manufacturing Technology, 21(10), 754-759, 2003
Hou TH. Hu WC. “An integrated MOGA approach to determine the pareto-optimal kanban number and size for a JIT system”. Expert Systems with Applications, 38(5), 5912-5918, 2011.
Lee I. “Evaluating artificial intelligence heuristics for a flexible kanban system: Simultaneous kanban controlling and scheduling”. International Journal of Production Research, 45(13), 2859-2873, 2007.
Huang CC. Kusiak A. “Overview of kanban systems”. International Journal of Computer Integrated Manufacturing, 9(3), 169-189, 1996.
Kumar CS, Panneerselvam R. “Literature review on JIT-Kanban system”. The International Journal of Advanced Manufacturing Technology, 32(3), 393-408, 2007.
Junior ML, Filho MG. “Variations of the kanban system: Literature review and classification”. International Journal of Production Economics, 125(1), 13-21. 2010.
Aytug H. Dogan CA. Bezmez G. “Determining the number of kanbans: A simulation metamodeling approach”. Simulation, 67(1), 23-32, 1996.
Hurrion RD. “An example of simulation optimization using a neural network metamodel: Finding the optimum number of kanbans in manufacturing system”. Journal of Operations Research Society, 48(11), 1105-1112, 1997.
Gonzalez RPL, Framinan JM, Pierreval H. “Token-Based pull production control systems: An introductory overview”. Journal of Intelligent Manufacturing, 23(1), 5-22, 2012.
Tardif V, Maaseidvaag L. “An adaptive approach to controlling kanban systems”. European Journal of Operational Research, 132(2), 411-424, 2001.
Marand LLP, Sakata Y, Hirotani D, Morikawa K, Takahashi K. An Adaptive Kanban and Production Capacity Control Mechanism. Editors: Emmanouilidis C, Taisch M, Kiritsis D. Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services, 452-459, Berlin, Germany, Springer, 2013.
Takahashi K, Myreshka, Hirotani D. “Comparing CONWIP, synchronized CONWIP, and kanban in complex supply chains”. International Journal of Production Economics, 93-94, 25-40, 2005.
Shahabudeen P, Sivakumar GD. “Algorithm for the design of single-stage adaptive kanban system”. Computers & Industrial Engineering, 54(4), 800-820, 2008
Araz OU, Salum L. “A multi-criteria adaptive control scheme based on neural networks and fuzzy inference for DRC manufacturing systems”. International Journal of Production Research, 48(1), 251-270, 2010.
Zadeh LA. “Fuzzy sets”. Information and Control, 8, 338-353, 1965.
Mamdani EH. “Application of fuzzy algorithms for control of simple dynamic plant”. Proceedings of the Institution of Electrical Engineers, 121(12), 1585-1588, 1974.
Welch PD. The statistical Analysis of Simulation Results. Editor: Lavenberg SS. The Computer Performance Modeling Handbook, 268-328, New York, USA, Academic Press, 1983.