KALİTE VE MİKTAR BELİRSİZLİKLERİ ALTINDA GERİ DÖNÜŞÜM AĞ TASARIMI

Yıl 2013, Cilt: 12 Sayı: 23, 55 - 77, 01.06.2013

Berk Ayvaz Bersam Bolat

Öz

In recent years, reverse logistics (RL) has received increasing attentions in supply chain management area due to economic, political, and environmental reasons. In RL, the time, quantity, and quality of returned products have a high degree of uncertainty. Deterministic models for reverse network design lack the ability to incorporate such uncertainties. In this study, we considered reverse logistics network design (RLND) problem under return quantity and quality uncertainties. We presented multi-stage, multi-product and the capacity constraited two stage stochastic programing model to take into consideration uncertainties in RLND as a real world case study of waste of electric and the waste of electrical and electronic equipment recycling firm in Turkey to minimize total cost. Sample average approximation schema was developed in solution process. The results show that the developed two stage stochastic programming model provides acceptable solutions to make efficient decisions under quantity and quality uncertainties

Anahtar Kelimeler

Reverse Logistics Network Design, Stochastic Programming, Sample Average Approximation

KALİTE VE MİKTAR BELİRSİZLİKLERİ ALTINDA GERİ DÖNÜŞÜM AĞ TASARIMI

Öz

Günümüzde tersine lojistik (TL) önemli bir karlı ve sürdürülebilir iş stratejisi olarak giderek artan bir şekilde önem kazanmaktadır. İşletmeler ise politik, ekonomik ve çevresel baskılardan dolayı TL faaliyetlerini uygulamak zorunda kalmaktadırlar. TL ağlarının en karakteristik özelliği ise ağ tasarımında kullanılan bazı parametre değerlerinin bilinmediği durumlarda ortaya çıkan belirsizliktir. TL ağları geri dönen ürünün zaman, miktar ve kalitesi ile ilgili yüksek oranda belirsizlik içermektedir. Deterministik modeller ise doğasında belirsizlikleri içeren TL ağlarını tasarlamakta yetersiz kalmaktadır. Bu çalışmada belirsizlikler altında çok aşamalı, çok ürünlü, kapasite ve tesis sayısı kısıtlı iki aşamalı stokastik programlama modeli önerilmiştir. Önerilen modelin çözümünde örneklem yakınsama yaklaşımı şeması kullanılmıştır. Çalışmada geliştirilen genel TL ağ tasarım modeli Türkiye’de elektrikli ve elektronik atıkların geri dönüşümü alanında hizmet vermekte olan tersine lojistik firmasının ağ tasarım problemi için uygulanmıştır. Sonuçlar geliştirilen stokastik programlama modelinin ekonomik açıdan etkin olduğunu göstermekte ve belirsizlikleri gidererek yöneticilere stratejik yatırım kararı almada yardımcı olabileceğini göstermektedir

Anahtar Kelimeler

Tersine Lojistik Ağ Tasarımı, Stokastik Programlama, Örneklem Ortalama Yakınsaması

Kaynakça

• Amin, S.H. ve Zhang, G.,2012, A multi-objective facility location model for closed- loop supply chain network under uncertain demand and return, Applied Mathematical Modelling, 37, Issue 6, 4165–4176.
• Assavapokee, T., ve Wongthatsanekorn, W., 2012, Reverse production system infrastructure design for electronic products in the state of Texas. Computers & Industrial Engineering, 62, 129–140.
• Aydin, N. ve Murat A., 2012, A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem, Int. J. Production Economics (November 2012), doi:10.1016/j.ijpe.2012.10.019.
• Barros, L., Dekker, R. ve Scholten, V., 1998, A two-level network for recycling sand: a case study. European Journal of Operational Research, 110, 199-214.
• Chouinard, M., D’Amoursa, S. ve Ait-Kadia, D., 2008,A Stochastic Programming Approach For Designing Supply Loops, Int. J. Production Economics, 113, 657–677 Dantzig, G. B. ve Infanger, G., 2011, A Probabilistic Lower Bound for Two-Stage Stochastic Programs, International Series in Operations Research & Management Science, 150, 13-35
• Dekker, R., Inderfurth, K., van Wassenhove, L.N., Fleischmann, M. , 2004,Reverse logistics: quantitative models for closed-loop supply chains,Springer-Verlag, Berlin Heidelberg.
• Demirel, Ö. N. ve Gökçen, H., 2008, Geri kazanımlı imalat sistemleri için lojistik ağı tasarımı: literatür araştırması, Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 23(4), 903-912.
• Denizel, M., Ferguson, M. ve Souza, G.C., 2010,.Multiperiod Remanufacturing Planning With Uncertain Quality of Inputs, IEEE Transactions On Engineering Management, 57, No. 3, 394-404.
• El-Sayed, M., Afia, N. ve El-Kharbotly, A., 2010, A stochastic model for forward- reverse logistics network design under risk, Computers & Industrial Engineering, 58, 423-431.
• Fleischmann, M., Krikke, H.R., Dekker, R. ve Flapper, S.D.P., 2000, A characterization of logistics networks for product recovery, Omega, 28, 653-666.
• Fonseca M.C., Sánchez A.G., Mier M.O. ve Da Gama F.S., 2010, A stochastic bi- objective location model for strategic reverse logistics, Business And Economics, Top 18(1), 158-184.
• Francas, D. ve Minner, S., 2009, Manufacturing network configuration in supply chains with product recovery , Omega, 37, 757 – 769.
• Gomes, M. I., Povoa, A. P. B. ve Novais, A. Q. , 2011,Modelling a recovery network for WEEE: A case study in Portugal, Waste Management, 31, 1645–1660.
• Hiller, F.S. ve Lieberman G.J., 2001, Solution Manuel to accompany Introduction to Operation research 7th Edition, McGraw Hill, Boston.
• Hong, I.H., Assavapokee, T., Ammons, J., Boelkins, C., Gilliam, K., Oudit, D., Realff, M. J., Vannícola, J. M. ve Wongthatsanekorn W., 2006, Planning the e- Scrap Reverse Production System Under Uncertainty in the State of Georgia: A Case Study, IEEE Transactions On Electronics Packaging Manufacturing, Vol. 29, No. 3, 150-162.
• Ilgin, M.A. ve Gupta, S. M., 2010, Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art, Journal of Environmental Management, 91, 563–591.
• Kall, P. ve Wallace, S.W. , 1994, Stochastic programming, John Wiley and Sons, Chichester, U.K.
• Kara, S. S. ve Onut, S., 2010a, A two-stage stochastic and robust programming approach to strategic planning of a reverse supply network: The case of paper recycling, Expert Systems with Applications, 37, 6129–6137.
• Kara, S. S. ve Onut, S., 2010b, A stochastic optimization approach for paper recycling reverse logistics network design under uncertainty, Int. J. Environ. Sci. Tech., 7 (4), 717-730.
• Kara, S., Rugrungruang, F., Kaebernick, H., 2007, Simulation modeling of reverse logistics networks, Int. J. Production Economics, 106, 61–69.
• Kleywegt, A. J., Shapiro A. ve De Mello ,T. H., 2001, The sample average approximation method for stochastic discrete optimizatıon, SIAM Journal of Optimization, 12, No. 2, 479–502.
• Lee, D., Dong, M. ve Bian, W., 2010, The design of sustainable logistics network under uncertainty, Int. J. Production Economics,128, 159–166.
• Lee, D.H. ve Dong, M., 2009, Dynamic network design for reverse logistics operations under uncertainty, Transportation Research, Part E. 45, 61-71.
• Lee, Y.J. , 2009, Integrated Forward-Reverse Logistics System Design: An Empirical Investigation, Doctor Of Philosophy Washington State University College of Business, May 2009.
• Li, R. C. ve Tee, T. J. C. , 2012, A Reverse Logistics Model For Recovery Options Of Ewaste Considering the Integration of the Formal and Informal Waste Sectors, Procedia - Social and Behavioral Sciences,40, 788 – 816.
• Listeş, O., 2002, A decomposition approach to a stochastic model for supply-and- return network design, Erasmus, 1-27.
• Listeş, O., 2007, A generic stochastic model for supply-and-return network design, Computers & Operations Research, 34, 417-442.
• Listeş, O. ve Dekker, R., 2005, A stochastic approach to a case study for product recovery network design, European Journal of Operational Research,160, 268-87.
• Long, Y., Lee L.H. ve Chew, E. P., 2012, The sample average approximation method for empty container repositioning with uncertainties, European Journal of Operational Research, 222, Issue 1, 65–75.
• Mak, W.K., Morton, D. P. ve Wood, R. K. ,1999, Monte Carlo bounding techniques for determining solution quality in stochastic programs, Operations Research Letters, 24, 47-56.
• Mier, M.O., Hipolito, J.D. ve Sanchez, A.G., 2009, Scatter search for locating a treatment plant and the necessary transfer centers in a reverse network, Metaheuristics in the service industry, lecture notes in economics and mathematical systems, 624, 63-81.
• Min, H., Ko, H.J. ve Ko, C.S., 2006, A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns,Omega, 34, 56-69.
• Patil, G. ve Ukkusuri, S., 2011,Sample Average Approximation Technique for
• Flexible Network Design Problem, Journal of Computing in Civil Engineering, 25(3), 254–262.
• Pishvaee, M.S., Farajani, R.Z. ve Dullaert, W., 2010a, A memetic algorithm for biobjective integrated forward/reverse logistics network design, Computers & Operations Research, 37, 1100-1112.
• Pishvaee, M.S., Jola, F., and Razmi, J., 2009, A Stochastic Optimization Model for Integrated Forward/Reverse Logistics Network Design, Journal of Manufacturing Systems, 28 (4), 107-114.
• Pishvaee, M.S., Rabbani, M. ve Torabi, S.A., 2011, A robust optimization approach to closed-loop supply chain network design under uncertainty, Applied Mathematical Modelling, 35, 637–649.
• Qin, Z. ve Ji, X., 2010, Logistics network design for product recovery in fuzzy environment, European Journal of Operational Research, 202, 479-490.
• Qiushuang, C. ve Qiaolun, G., 2007, Analysis of Quantity Uncertainty of Returns on Remanufacturing Logistic Network Using a Stochastic Programming Model, IEEE International Conference on Control and Automation, Guangzhou, China.
• Ramezani, M., Bashiri, M. ve Moghaddam, R. T., 2013, A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level, Applied Mathematical Modelling,37, 328–344.
• Realff, M. J., Amnions, J. C. ve Newton, D., 2000, Strategic design of reverse production systems, Computers and Chemical Engineering, 24, 991-996.
• Realff, M.J., Ammons, J. C. ve Newton, D. J., 2004,Robust reverse production system design for carpet recycling, IIE Transactions,36, Issue 8, 767-776.
• Salema, M.I.G., Barbosa-Povoa, A.P. ve Novais, A.Q., 2007, An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty, European Journal of Operational Research, 179, 1063-1077.
• Santoso, T., Ahmed, S., Goetschalckx, M. ve Shapiro A., 2005, A stochastic programming approach for supply chain network design under uncertainty, European Journal of Operational Research,167, 96–115.
• Schütz, P., Tomasgard, A. ve Ahmed S., 2009, Supply chain design under uncertainty using sample average approximation and dual decomposition, European Journal of Operational Research, 199, 409–419.
• Shapiro, A. ve De Mello, T. H., 1998, A simulation-based approach to two-stage stochastic programming, Mathematical Programming, 81, 301-325.
• Shih, L.H., 2001, Reverse logistics system planning for recycling electrical appliances and computers in Taiwan. Resources, Conservation and Recycling, 32, 55-72.
• Şengül, Ü., 2009, Tersine Lojistik Kavramı Ve Tersine Lojistik Ağ Tasarımı, 10. Ekonometri ve İstatistik Sempozyumu, Palandöken/Erzurum 27-29 Mayıs 2009.
• Taha, H.A., 2003, Operation Research: An Introduction, 8th Edition, Pearson Prentice Hall, New Jersey.
• Verweij, B., Ahmed, S., Kleywegt, A. J., Nemhauser, G. ve Shapiro A., 2002, The Sample Average Approximation Method Applied to Stochastic Routing Problems: A Computational Study, Computational Optimization and Applications, 24, 289–333.
• Wang, X. ve Zhao, L., 2009, Network Design of Reverse Logistics Integrated with Forward Logistics, Power and Energy Engineering Conference, APPEEC 2009,Asia-Pacific .1, 27-31. Wei-min,
• Manufacturing/Remanufacturing
• Programming, International Conference on Management Science & Engineering (16th), pp.867-873, doi: 10.1109/ICMSE.2009.5318205. Optimal Based Logistics on
• Uncertain Yongsheng Z. ve Shouyang W., 2008, Generic Model of Reverse Logistics Network Design, Journal of Transportation Systems Engineering and Information Technology, 8, Issue 3, 71-78.