A Multi-Objective Supplier Selection and Order Allocation Model for Green Supply Chains

Yıl 2016, Cilt: 4 Sayı: 3, 87 - 96, 25.05.2016

Gulfem Tuzkaya , Huseyin Selcuk Kilic , Canan Aglan

Öz

Considering the limited natural resources, increased consumption level of the world becomes an important problem for the human beings. This fact enforces the governments to make some regulations for the environment. Also, increased consumer consciousness puts a pressure on the companies for being more environmentally friendly. Companies are not only responsible for their own production environment, but also responsible for their suppliers. Hence, the supplier selection and order allocation processes have to be greener. For this purpose, in this study a multi-objective supplier selection and order allocation model is proposed for green supply chains regarding multi-item and multi-supplier case. So as to validate the proposed methodology including three stages, a numerical example is provided by inspiring from the real applications in Turkey.

Kaynakça

• Aissaoui, N., Haouari, M., Hassini, E. (2007). Supplier selection and order lot sizing modeling: A review. Computers & operations research, 34(12), 3516-3540.
• Amid, A., Ghodsypour S.H., O’Brien, C. (2011). A weighted max-min model for fuzzy multi-objective supplier selection in a supply chain. International Journal of Production Economics, 131(1), 139-145.
• Amid, A., Ghodsypour, S. H.,O’brien, C. (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International Journal of Production Economics, 104(2), 394-407.
• Amin, S. H., Zhang, G.(2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert Systems with Applications, 39(8), 6782-6791.
• Ashayeri, J., Tuzkaya, G. (2011). Design of demand driven return supply chain for high-tech products. Journal of Industrial Engineering and Management, 4(3), 481-503.
• Awasthi, A., Chauhan, S. S., Goyal, S. K. (2010). A fuzzy multicriteria approach for evaluating environmental performance of suppliers. International Journal of Production Economics, 126(2), 370-378.
• Bai, C., Sarkis, J. (2013). Flexibility in reverse logistics: a framework and evaluation approach.. J. Clean. Prod. 47 (1), 306-316.
• Bevilacqua, M., Ciarapica, F. E., Giacchetta, G. (2006). A fuzzy-QFD approach to supplier selection. Journal of Purchasing and Supply Management, 12(1), 14-27.
• Bonney, M., Jaber, M.Y. (2011). Environmentally responsible inventory models: non-classical models for a non-classical era. Int.J.Prod.Econ., 133(1),43–53.
• Brandenburg, M., Govindan, K., Sarkis, J., Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European Journal of Operational Research, 233(2), 299-312.
• Carter, C. R., Ellram, L. M. (1998). Reverse logistics: A review of the literature and framework for future investigation. International Journal of Business Logistics, 19(1), 85–102.
• Chou, S. Y., Chang, Y. H. (2008). A decision support system for supplier selection based on a strategy-aligned fuzzy SMART approach. Expert systems with applications, 34(4), 2241-2253.
• Dotoli, M., Epicoco, N., Falagario, M. (2016). A fuzzy technique for supply chain network design with quantity discounts. International Journal of Production Research, 1-23.
• Efendigil, T., Önüt, S., Kongar, E. (2008). A holistic approach for selecting a third-party reverse logistics provider in the presence of vagueness. Computers & Industrial Engineering, 54(2), 269-287.
• Fleischmann, M. (2001). Quantitative models for reverse logistics. Lecture Notes in Economics and Mathematical Systems, 501.
• Guarnieri, P., Sobreiro, V. A., Nagano, M. S., & Serrano, A. L. M. (2015). The challenge of selecting and evaluating third-party reverse logistics providers in a multicriteria perspective: a Brazilian case. Journal of Cleaner Production, 96, 209-219.
• Gupta, P., Govindan, K., Mehlawat, M. K., Kumar, S. (2016). A weighted possibilistic programming approach for sustainable vendor selection and order allocation in fuzzy environment. The International Journal of Advanced Manufacturing Technology, 1-20.
• Ho, W., Xu, X., Dey, P.K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. European Journal of Operational Research, 202, 16-24.
• Kannan, D., Khodaverdi, R., Olfat, L., Jafarian, A., Diabat, A. (2013) Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner Production, 47, 355-367.
• Kannan, G., Pokharel, S., Sasi Kumar, P. (2009). A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider. Resources, conservation and recycling, 54(1), 28-36.
• Kilic, H. S. (2013). An integrated approach for supplier selection in multi-item/multi-supplier environment. Applied Mathematical Modelling, 37(14), 7752-7763.
• Kilic, H. S., Cebeci, U., Ayhan, M. B. (2015). Reverse logistics system design for the waste of electrical and electronic equipment (WEEE) in Turkey, Resources, Conservation and Recycling, 95, 120-132.
• Kongar, E., Gupta, S. (2006). Disassembly to order system under uncertainty. Omega, 34, 550-561.
• Lee AHI, Kang HY, Hsu CF, Hung HC. (2009). A green supplier selection model for high-tech industry. Expert Systems with Applications, 36, 7917–27.
• Liang, T.-F. (2010) Applying fuzzy goal programming to project management decisions with multiple goals in uncertain environments. Expert Systems with Applications, 37, 8499-8507.
• Mak, T., Nebebe, F. (2016) Factor Analysis and Methods of Supplier Selection. International Journal of Supply Chain Management, 5(1), 1-9.
• Min, H., Ko, H. J. (2008). The dynamic design of a reverse logistics network from the perspective of third-party logistics service providers. International Journal of Production Economics, 113(1), 176-192.
• Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert Systems with Applications, 42(15), 6237-6254.
• Noci, G. (1997). Designing ‘Green’ vendor rating systems for the assessment of a supplier’s environmental performance. European Journal of Purchasing and Supply Management, 3(2), 103–114.
• Prakash, C., Barua, M. K. (2016). A combined MCDM approach for evaluation and selection of third-party reverse logistics partner for Indian electronics industry. Sustainable Production and Consumption, 7, 66-78.
• Saaty, T.L. (2008). Decision making with the analytic hierarchy process. Int. J. Services Sciences, Vol. 1, No. 1, pp.83–98.
• Saaty, T.L. (2005). Theory and Applications of the Analytic Network Process. Pittsburgh, PA: RWS Publications.
• Saaty, T.L. (1994). How to make a decision: the analytic hierarchy process. Interfaces, Vol. 24, No. 6, pp.19–43.
• Saaty, T. L. (1980). The Analytic Hierarchy Process. New York: McGraw - Hill.
• Sarkis, J. (1998). Evaluating environmentally conscious business practices. European journal of operational research, 107(1), 159-174.
• Sbihi, A., Eglese, R. W. (2007). Combinatorial optimization and green logistics. 4OR, 5(2), 99-116.
• Seuring, S., Müller, M. (2008). Core issues in sustainable supply chain management a delphi study. Business Strategy and the Environment, 17(8), 455–466.
• 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.
• Shen, L., Olfat, L., Govindan, K., Khodaverdi, R., Diabat, A. (2013). A fuzzy multi criteria approach for evaluating green supplier's performance in green supply chain with linguistic preferences. Resources, Conservation and Recycling, 74, 170-179.
• Sheu, J. B., Chou, Y. H., Hu, C. C. (2005). An integrated logistics operational model for green-supply chain management. Transportation Research Part E: Logistics and Transportation Review, 41(4), 287-313.
• Shu, M. H., Wu, H. C. (2009). Quality-based supplier selection and evaluation using fuzzy data. Computers & Industrial Engineering, 57(3), 1072-1079.
• Seuring, S., Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of Cleaner Production, 16(15), 1699–1710.
• Talluri, S., Sarkis, J. (2002). A model for performance monitoring of suppliers. International Journal of Production Research, 40(16), 4257–4269.
• Tahirov, N., Hasanov, P., Jaber, M. Y. (2016). Optimization of closed-loop supply chain of multi-items with returned subassemblies. International Journal of Production Economics, 174, 1-10.
• Trisna, T., Marimin, M., Arkeman, Y., Sunarti, T. (2016). Multi-objective optimization for supply chain management problem: A literature review. Decision Science Letters, 5(2), 283-316.
• Tuzkaya, G. (2013). An intuitionistic fuzzy Choquet integral operator based methodology for environmental criteria integrated supplier evaluation process. International Journal of Environmental Science and Technology, 10, 423-432.
• Wang Chen, H. M., Chou, S. Y., Luu, Q. D., Yu, T. H. K. (2016). A Fuzzy MCDM Approach for Green Supplier Selection from the Economic and Environmental Aspects. Mathematical Problems in Engineering, 2016, 1-10.
• Wang, J. W., Cheng, C. H., Huang, K. C. (2009). Fuzzy hierarchical TOPSIS for supplier selection. Applied Soft Computing, 9(1), 377-386.