Öz
Üretim çizelgeleme işletmelerin üretim planlama ve kontrol bölümlerinin başarısında çok önemli rol oynamaktadır. Çizelgeleme optimizasyonu, sahip olduğu çözüm uzayı nedeniyle çözümü zor ve zaman alan optimizasyon problemlerinden biridir. Kombinasyona dayalı zorluğundan geçmişten günümüze araştırmacıların ilgi odağı olmuştur. İlk araştırmacılar problemleri kesin matematiksel yöntemler ile çözmeye çalışırken, problemler karmaşıklaştıkça bu çözümler uygun zamanlarda sonuç vermediği görülmüştür. Bu nedenle, araştırmacılar çizelgeleme problemlerini çözmek için sezgisel ve meta sezgisel yöntemler geliştirmiştir. Bu çalışmada endüstriyel ürünler imalatı alanında faaliyet gösteren bir firmanın çok fonksiyonlu makinalardan oluşan bir üretim hattının optimizasyonu problemi ele alınmıştır. Çalışma kapsamında öncelikle üretim modeli oluşturulmuş, sonrasında ise genetik algoritma kullanılarak oluşturulan optimizasyon modeli ile çizelgeleme problemi çözülmeye çalışılmıştır.
Anahtar Kelimeler
Kaynakça
- Borovska P. (2006): “Solving the Travelling Salesman Problem in Parallel by Genetic Algorithm on Multicomputer Cluster”, International Conference on Computer Systems and Technologies - CompSysTech‟06, 15-16 June 2006, University of Veliko Tarnovo, Bulgaria
- Buzacott, J. A., Corsten, H., Gössinger, R. ve Schneider, H. M., (2012). “Production Planning and Control: Basics and Concepts”, Oldenbourg Verlag.
- Coley, D.A. (1999): An Introduction to Genetic Algorithms for Scientists and Engineers, World Scientific.
- Gen M. ve Cheng R. (2000), “Genetic algorithms and engineering optimization”, John Wiley & Sons, Inc.
- Gonçalves, J. F., de Magalhães Mendes, J. J., & Resende, M. G. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European journal of operational research, 167(1), 77-95.
- Graves, S. , C. (1981): “A review of Production Scheduling”, Operations Research, 29(4), Temmuz-Ağustos, ss. 646-675.
- Hamilton, W.D. ve Ridley, M. (2005): Narrow roads of gene land: the collected papers of W.D. Hamilton, Oxford University Press.
- Holland J.H. ,1975, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan.
- Hou, E. S., Ansari, N., ve Ren, H. (1994). A genetic algorithm for multiprocessor scheduling. IEEE Transactions on Parallel and Distributed systems, 5(2), 113-120.
- Kamauff J., 2010, Manager’s Guide to Operations Management, New York, McGraw-Hill
- Kocamaz M. ve Çiçekli U.G, 2010, Paralel Makinaların Genetik Algoritma İle Çizelgelenmesinde Mutasyon Oranının Etkinliği, Ege Akademik Bakış, 10(1):199-210.
- Pinedo M., 2008, Scheduling Theory Algorithms and Systems ,Prentice Hall/Springer
- Reeves C.R., Rowe J.E., 2003, Genetic Algorithms: Principles and Perspectives; A Guide to GA Theory, Kluwer Academic Publishers, Dordrecht
- Russell, R.S., Taylor III, B.W. (2000): Operation Management, 4th Edition, Prentice Hall, New Jersey.
- Sheikh, K. (2003): Manufacturing Resource Planning(MRP II) With an introduction to ERP, SCM, and CRM, McGraw-Hill.
- Sivanandam S.N. ve Deepa S.N., 2008, Introduction to Genetic Algorithms , Springer
Öz
Production Scheduling plays a crucial role in the success of production planning and control departments of businesses. Scheduling optimization is one of the difficult and time-consuming optimization problems to solve due to the solution space. It has become the focus of researchers' interest due to its combination-based difficulty. While the first researchers tried to solve the problems with classical mathematical methods, these solutions were found not to work appropriately as the problems became more complicated. For this reason, researchers have developed heuristic and meta-heuristic methods to solve scheduling problems. In this study, the optimization problem of production line consists of a multifunctional machine operating in the industrial goods manufacturing field. First, the production model was created, and then the scheduling problem was tried to be solved by the optimization model created by using genetic algorithm.
Anahtar Kelimeler
Kaynakça
- Borovska P. (2006): “Solving the Travelling Salesman Problem in Parallel by Genetic Algorithm on Multicomputer Cluster”, International Conference on Computer Systems and Technologies - CompSysTech‟06, 15-16 June 2006, University of Veliko Tarnovo, Bulgaria
- Buzacott, J. A., Corsten, H., Gössinger, R. ve Schneider, H. M., (2012). “Production Planning and Control: Basics and Concepts”, Oldenbourg Verlag.
- Coley, D.A. (1999): An Introduction to Genetic Algorithms for Scientists and Engineers, World Scientific.
- Gen M. ve Cheng R. (2000), “Genetic algorithms and engineering optimization”, John Wiley & Sons, Inc.
- Gonçalves, J. F., de Magalhães Mendes, J. J., & Resende, M. G. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European journal of operational research, 167(1), 77-95.
- Graves, S. , C. (1981): “A review of Production Scheduling”, Operations Research, 29(4), Temmuz-Ağustos, ss. 646-675.
- Hamilton, W.D. ve Ridley, M. (2005): Narrow roads of gene land: the collected papers of W.D. Hamilton, Oxford University Press.
- Holland J.H. ,1975, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan.
- Hou, E. S., Ansari, N., ve Ren, H. (1994). A genetic algorithm for multiprocessor scheduling. IEEE Transactions on Parallel and Distributed systems, 5(2), 113-120.
- Kamauff J., 2010, Manager’s Guide to Operations Management, New York, McGraw-Hill
- Kocamaz M. ve Çiçekli U.G, 2010, Paralel Makinaların Genetik Algoritma İle Çizelgelenmesinde Mutasyon Oranının Etkinliği, Ege Akademik Bakış, 10(1):199-210.
- Pinedo M., 2008, Scheduling Theory Algorithms and Systems ,Prentice Hall/Springer
- Reeves C.R., Rowe J.E., 2003, Genetic Algorithms: Principles and Perspectives; A Guide to GA Theory, Kluwer Academic Publishers, Dordrecht
- Russell, R.S., Taylor III, B.W. (2000): Operation Management, 4th Edition, Prentice Hall, New Jersey.
- Sheikh, K. (2003): Manufacturing Resource Planning(MRP II) With an introduction to ERP, SCM, and CRM, McGraw-Hill.
- Sivanandam S.N. ve Deepa S.N., 2008, Introduction to Genetic Algorithms , Springer
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Bölüm | Beşeri Bilimler Sayısı |
| Yazarlar | |
| Yayımlanma Tarihi | 26 Mart 2021 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 19 Sayı: 1 |
