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TEDARK ZİNCİR ULAŞTIRMA PROBLEMİ İÇİN BİR SEZGİSEL ÇÖZÜM: GENETİK ALGORİTMA YAKLAŞIM

Year 2009, Volume: 1 Issue: 27, 43 - 65, 01.12.2009

Abstract

Tedarik zinciri ağ tasanm problemi, tedarik zincirini oluşturan öğelerin sayılarının ve konumlarının tespiti, birbirleri arasındaki ürün akış miktanmn belirlenmesi şeklinde tanımlanabilir. Tasanm probleminin ana hedefi bu planlama faaliyetlerinin minimum mali¬yet ile gerçekleştirilmesi olarak tanımlanabilir. Ulaştırma problemi olarak formüle edilebi¬len bu problemde amaç değişik arz noktalarından değişik talep noktalarına toplam maliyeti en küçükleyecek şekilde ürünün nasıl taşınacağının tespit edilmesidir. Bu çalışmada stan¬dart lineer ulaştırma problemi temelinde genetik algoritmalar uygulanmış ve ulaştırma probleminin genetik gösterimi ile karşılaşılan zorluklar açıklanmıştır. Problemin genetik gösteriminde kullanılan vektör gösterim yapısı ve matris gösterim yapıları genetik operatör¬lerin uygulanması ve amaç fonksiyonun değerlendirilmesi açandan incelenmiştir. Yapılan analizler ile ulaştırma probleminin çözümünde vektör ve matris gösterimlerinin etkinlikleri belirli bir iterasyon sayanda optimum çözüme yaklaşma amacı açandan incelenmiştir. Sonuç olarak ulaştırma probleminin genetik algoritmalar ile çözümünde matris gösterimin vektör gösterime göre daha başanlı sonuçlar ürettiği ve aynca kod basitliği ve uygulanabi¬lirliği açandan da daha üstün olduğu belirlenmiştir

References

  • Amiri, A., Designing a distribution network in a supply chain system: Formulation and efficient solution procedure, European Journal of Operational Research, Vol. 171, No.2, 2006, pp. 567 576.
  • Aytug, H., Khouja, M., and, Vergara, F. E.., Use of genetic algorithms to solve production and operations management: a review, International Journal of Production Researches, Vol. 41, No.17, 2003, pp. 3955 4009.
  • Beamon, B. M., Supply chain design and analysis: models and methods, International Journal of Production Economics, Vol. 55, 1998, pp.281 294.
  • Chan, F. T. S., Chung, S. H., and, Wadhwa, S., A hybrid genetic algorithm for production and distribution, Omega, Vol. 33, 2004, pp. 345 355.
  • Chen, C., and, Lee, W., Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices, Computers and Chemical Engineering, Vol. 28, 2004, pp. 1131 1144.
  • Dimopoulos, C., and, Zalzala, A. M. S., Recent developments in evolutionary computation for manufacturing optimization: Problems, solutions and comparisons, IEEE Transactions on Evolutionary Computation, Vol. 4, No.2, 2000, pp.93 113.
  • Erenguc, S. S., Simpson, N. C., and, Vakharia, A. J., Integrated production/distribution planning in supply chains: An invited review, European Journal of Operational Research, Vol. 115, 1999, pp.219 236. Erol, I., and, Ferrell, W. G. Jr., A methodology to support decision making across the supply chain of an industrial distributor, International Journal of Production Economics, Vol.89, 2004, pp.119 129.
  • Gen, M., and, Cheng, R., Genetic algorithms and engineering optimization. New York: Wiley, NY, 2000
  • Gen, M., and, Syarif, A., Hybrid genetic algorithm for multi-time period production / distribution planning, Computers and Industrial Engineering, Vol. 48, No. 4, 2005, pp. 799 809.
  • Goldberg D. E., Genetic Algorithms in Search, Optimization & Machine Learning.Reading, Addison Wesley, MA, 1989.
  • Guillen, G., Mele, F. D., Bagajewicz, M. J., Espuna, A., and, Puigjaner, L., Multiobjective supply chain design under uncertainty, Chemical Engineering Science, Vol. 60, 2005, pp.1535 1553.
  • Jayaraman, V., and, Pirkul, H., Planning and coordination of production and distribution facilities for multiple commodities, European Journal of Operational Research, Vol. 133, 2001, pp. 394 408.
  • Jayaraman, V., and, Ross, A., A simulated annealing methodology to distribution network design and management, European Journal of Operational Research, Vol. 144, 2003, pp. 629 645.
  • Karao lan, ., Alt parmak, F., Konkav Maliyetli Ula t rma Problemi için Genetik Algoritma Tabanl Sezgisel bir Yakla m, Gazi Üniversitesi Mimarl k Mü- hendislik Fakültesi Dergisi, Cilt.20, 2005, sayfa: 443-454.
  • Paksoy, T., Tedarik Zinciri Yönetiminde Da t m A lar n n Tasar m Ve Optimizasyonu: Malzeme htiyaç K s t Alt nda Stratejik Bir Üretim-Da t m Modeli, Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Say :14, 2005, sayfa: 435-454.
  • Pontrandolfo, P., and, Okogbaa, O. G., Global manufacturing: a review and a framework for planning in a global corporation, International Journal of Production Research, Vol. 37, No. 1, 1999, pp. 1 19.
  • Sabri, E. H.,and, Beamon, B. M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design,Omega,Vol.28,2000,pp. 581 598.
  • Syam, S. S., A model and methodologies for the location problem with logistical components, Computers and Operations Research, Vol. 29, 2002, pp.1173 1193.
  • Syarif, A., Yun, Y., and, Gen, M., Study on multi-stage logistics chain network: A spanning tree-based genetic algorithm approach, Computers and Industrial Engineering, Vol. 43, 2002, pp. 299 314.
  • Taha, H.A., Operations Reseach: An Introduction, Prentice Hall, NJ.,1994.
  • Truong, T. H., and, Azadivar, F., Optimal design methodologies for con.guration of supply chains, International Journal of Production Researches, Vol. 43, No.11, 2005, pp. 2217 2236.

AN HEURISTIC SOLUTION FOR SUPPLY CHAIN TRANSPORT PROBLEM: GENETICS ALGORITHMS APPROACH

Year 2009, Volume: 1 Issue: 27, 43 - 65, 01.12.2009

Abstract

Supply chain network design problem can be defined as determining the locations, number of supply chain members and the amount of product flows between the chain members. The main purpose of the design problem can be defined as realizing these planning activities supply points to different demand points with minimum cost. In this study, genetic algorithms were applied to standard linear transportation problems and difficulties were explained by using the genetic illustration of the transportation problem. Vector structure and matrix structure of the genetic problem are examined in terms of application of genetic operators and evaluation of objective function. By analyzing the solutions of the transportation problem, vector and matrix structure efficiencies are examined in terms of achieving the optimum solution by specific iteration numbers. The article concludes that matrix structure of genetic problems is superior to vector structure in terms of providing better solutions, code simplicity and applicability

References

  • Amiri, A., Designing a distribution network in a supply chain system: Formulation and efficient solution procedure, European Journal of Operational Research, Vol. 171, No.2, 2006, pp. 567 576.
  • Aytug, H., Khouja, M., and, Vergara, F. E.., Use of genetic algorithms to solve production and operations management: a review, International Journal of Production Researches, Vol. 41, No.17, 2003, pp. 3955 4009.
  • Beamon, B. M., Supply chain design and analysis: models and methods, International Journal of Production Economics, Vol. 55, 1998, pp.281 294.
  • Chan, F. T. S., Chung, S. H., and, Wadhwa, S., A hybrid genetic algorithm for production and distribution, Omega, Vol. 33, 2004, pp. 345 355.
  • Chen, C., and, Lee, W., Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices, Computers and Chemical Engineering, Vol. 28, 2004, pp. 1131 1144.
  • Dimopoulos, C., and, Zalzala, A. M. S., Recent developments in evolutionary computation for manufacturing optimization: Problems, solutions and comparisons, IEEE Transactions on Evolutionary Computation, Vol. 4, No.2, 2000, pp.93 113.
  • Erenguc, S. S., Simpson, N. C., and, Vakharia, A. J., Integrated production/distribution planning in supply chains: An invited review, European Journal of Operational Research, Vol. 115, 1999, pp.219 236. Erol, I., and, Ferrell, W. G. Jr., A methodology to support decision making across the supply chain of an industrial distributor, International Journal of Production Economics, Vol.89, 2004, pp.119 129.
  • Gen, M., and, Cheng, R., Genetic algorithms and engineering optimization. New York: Wiley, NY, 2000
  • Gen, M., and, Syarif, A., Hybrid genetic algorithm for multi-time period production / distribution planning, Computers and Industrial Engineering, Vol. 48, No. 4, 2005, pp. 799 809.
  • Goldberg D. E., Genetic Algorithms in Search, Optimization & Machine Learning.Reading, Addison Wesley, MA, 1989.
  • Guillen, G., Mele, F. D., Bagajewicz, M. J., Espuna, A., and, Puigjaner, L., Multiobjective supply chain design under uncertainty, Chemical Engineering Science, Vol. 60, 2005, pp.1535 1553.
  • Jayaraman, V., and, Pirkul, H., Planning and coordination of production and distribution facilities for multiple commodities, European Journal of Operational Research, Vol. 133, 2001, pp. 394 408.
  • Jayaraman, V., and, Ross, A., A simulated annealing methodology to distribution network design and management, European Journal of Operational Research, Vol. 144, 2003, pp. 629 645.
  • Karao lan, ., Alt parmak, F., Konkav Maliyetli Ula t rma Problemi için Genetik Algoritma Tabanl Sezgisel bir Yakla m, Gazi Üniversitesi Mimarl k Mü- hendislik Fakültesi Dergisi, Cilt.20, 2005, sayfa: 443-454.
  • Paksoy, T., Tedarik Zinciri Yönetiminde Da t m A lar n n Tasar m Ve Optimizasyonu: Malzeme htiyaç K s t Alt nda Stratejik Bir Üretim-Da t m Modeli, Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Say :14, 2005, sayfa: 435-454.
  • Pontrandolfo, P., and, Okogbaa, O. G., Global manufacturing: a review and a framework for planning in a global corporation, International Journal of Production Research, Vol. 37, No. 1, 1999, pp. 1 19.
  • Sabri, E. H.,and, Beamon, B. M., A multi-objective approach to simultaneous strategic and operational planning in supply chain design,Omega,Vol.28,2000,pp. 581 598.
  • Syam, S. S., A model and methodologies for the location problem with logistical components, Computers and Operations Research, Vol. 29, 2002, pp.1173 1193.
  • Syarif, A., Yun, Y., and, Gen, M., Study on multi-stage logistics chain network: A spanning tree-based genetic algorithm approach, Computers and Industrial Engineering, Vol. 43, 2002, pp. 299 314.
  • Taha, H.A., Operations Reseach: An Introduction, Prentice Hall, NJ.,1994.
  • Truong, T. H., and, Azadivar, F., Optimal design methodologies for con.guration of supply chains, International Journal of Production Researches, Vol. 43, No.11, 2005, pp. 2217 2236.
There are 21 citations in total.

Details

Other ID JA78BD66BT
Journal Section Makaleler / Articles
Authors

Ali İhsan Özdemir This is me

Gökhan Seçme This is me

Publication Date December 1, 2009
Submission Date December 1, 2009
Published in Issue Year 2009 Volume: 1 Issue: 27

Cite

APA Özdemir, A. İ., & Seçme, G. (2009). TEDARK ZİNCİR ULAŞTIRMA PROBLEMİ İÇİN BİR SEZGİSEL ÇÖZÜM: GENETİK ALGORİTMA YAKLAŞIM. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1(27), 43-65.

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