Doğuş Üniversitesi Dergisi 1308-6979 Dogus University Yeşil Alan ve Dağıtım Planlaması için Çevreye Duyarlı Çok lı Bir Weber Problemi: Ağırlıklandırılmış Bulanık Toplama Yöntemi An Environmentally Conscious Multi-Objective Weber Problem for Green Location and Distribution Planning: A Fuzzy Weighted Additive Approach Engin Batuhan Eren Selçuk Üniversitesi Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Selçuklu, Konya Paksoy Turan Selçuk Üniversitesi Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü, Selçuklu, Konya 07 01 2019 20 2 1 13 Copyright © 2000, Dogus University Journal 2000 Dogus University Journal

Bu çalışmada, açılacak depoların konumunu ve ürünlerin dağıtım planlarını belirlemek amacıyla çok amaçlı bir Weber p-medyan problemi ele alınmıştır. Modelde dağıtım, birim taşıma maliyeti, karbon emisyonu ve hızları farklı olan üç tip araç ile yapılmaktadır. Talep ağırlıklı toplam ulaşım maliyeti, toplam teslimat süresi ve toplam karbon emisyonu gibi birbirleriyle çelişen 3 farklı amacın aynı anda enküçüklenmesi hedeflenmiştir. Amaçların ağırlıkları Analitik Hiyerarşi Süreci ile belirlenmiş ve çok amaçlı optimizasyon modeli, ağırlıklandırılmış bulanık toplama yöntemi ile çözülmüştür

In this study, a multi-objective Weber p-median problem is treated in order to determine the location of the warehouses to be opened and the distribution plans of products. The company carries out the distribution with three types of vehicles differing in unit transportation cost, carbon emission and velocity. Three conflicting objectives are aimed to be minimized, i.e.; the demand weighted total transportation cost, the total delivery time and the total carbon. We adopted a fuzzy weighted additive approach to deal with the multi-objective optimization function, in which the weights of each individual objective function are determined by Analytic Hierarchy Process.

 Analytic Hierarchy process fuzzy weighted additive solution approach multi-objective optimization P-median Analitik hiyerarşi süreci ağırlıklandırılmış bulanık toplama yöntemi çok amaçlı optimizasyon P-medyan Amid, A., Ghodsypour, S. H., & O’Brien, C. (2009). A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply Chain. International Journal of Production Economics, 121(2), 323-332. doi:http://dx.doi.org/10.1016/j.ijpe.2007.02.040 Arikan, F. (2013). A fuzzy solution approach for multi objective supplier selection. Expert doi:10.1016/j.eswa.2012.05.051 with Applications, 40(3), 947-952. Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. A. J. (2017). Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain. International Journal of Production Economics, 183, 409-420. doi:https://doi.org/10.1016/j.ijpe.2016.08.012 Bilir, C., Ekici, S. O., & Ulengin, F. (2017). An integrated multi-objective supply chain network and competitive facility location model. Computers & Industrial doi:https://doi.org/10.1016/j.cie.2017.04.020 108, 136-148. Chan, F. T. S., Jha, A., & Tiwari, M. K. (2016). Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-II with heuristics algorithm. Applied Soft Computing, 38, 978-987. doi:http://dx.doi.org/10.1016/j.asoc.2015.10.067 Fahimnia, B., Sarkis, J., & Eshragh, A. (2015). A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis. Omega, 54, 173-190. doi:10.1016/j.omega.2015.01.014 Intergovernmental Panel on Climate Change. (2014). Climate Change 2014–Impacts, Adaptation and Vulnerability: Regional Aspects: Cambridge University Press. Kadziński, M., Tervonen, T., Tomczyk, M. K., & Dekker, R. (2017). Evaluation of multi-objective optimization approaches for solving green supply chain design problems. Omega, 68, 168-184. doi:10.1016/j.omega.2016.07.003 Kavitha, C. a. V., C. (2013). Multi Objective Fuzzy Linear Programming Technique for Weighted Additive Model for Supplier Selection in Supply Chain Management. International Journal of Applied Mathematics and Informatics. Mehlawat, M. K., & Kumar, S. (2017). A multiobjective optimization model for optimal supplier selection in multiple sourcing environment. 2017, 26, 18. Ministry of Environment and Urbanization. (2011). National Climate Change Action Plan. Retrieved from Ankara;Turkey: Mohammed, A., & Wang, Q. (2017). The fuzzy multi-objective distribution planner for a green meat supply chain. International Journal of Production Economics, 184, 47-58. doi:https://doi.org/10.1016/j.ijpe.2016.11.016 Pan, W., Wang, F., Guo, Y., & Liu, S. (2015). A Fuzzy Multiobjective Model for Supplier Selection under Considering Stochastic Demand in a Supply Chain. Mathematical Problems in Engineering, 2015, 8. doi:10.1155/2015/174585 Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98. Sadeghi Rad, R., & Nahavandi, N. (2018). A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. Journal of Cleaner Production, 196, 1549- 1565. doi:https://doi.org/10.1016/j.jclepro.2018.06.034 Seifbarghy, M., Pourebrahim Gilkalayeh, A., & Alidoost, M. (2011). A Comprehensive Fuzzy Multiobjective Supplier Selection Model under Price Brakes and Using Interval Comparison Matrices. Journal of Industrial and Systems Engineering, 4(4), 224-244. Shaw, K., Shankar, R., Yadav, S. S., & Thakur, L. S. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain. Expert Systems with Applications, 39(9), 8182- 8192. Soleimani, H., Govindan, K., Saghafi, H., & Jafari, H. (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & doi:https://doi.org/10.1016/j.cie.2017.04.038 Engineering, 109, 191-203. Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., & Gholamnejad, S. (2016). A robust fuzzy optimization model for carbon- efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry. Journal of Cleaner Production, 113, 662- 673. doi:10.1016/j.jclepro.2015.10.074 Tiwari, R. N., Dharmar, S., & Rao, J. R. (1987). Fuzzy goal programming — An additive doi:http://dx.doi.org/10.1016/0165-0114(87)90111-4 Sets and Systems, 24(1), 27-34. Transport, & Environment. (2015). Lorry CO2 - Why Europe needs standards. Retrieved https://www.transportenvironment.org/sites/te/files/publications/2015_06_ Lorry_co2_briefing_update_US_PHASE_III.pdf from Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.