Document Type : Research Paper


1 Department of Business Administration, İzmir University of Economics, İzmir, Turkey

2 Opet Fuchs Oil Company,35600 Cigli-Izmir, Turkey


Effective design and management of Supply Chain Networks (SCN) support the production and delivery of products at low cost, high quality, high variety, and short lead times. In this study, a SCN is designed for an automotive company by integrating various approaches. The study has been carried out in two phases: The first phase involves selecting suppliers and distributors by using Data Envelopment Analysis (DEA) and integer-programming model. In the second phase, first the priority ranking of selected suppliers and distributors is determined using the Analytical Hierarchy Process (AHP) and then these priority rankings are integrated into the transportation models developed to identify the optimal routing decisions for all members of the supply chain. 


Main Subjects

[1]      Abdallah, T., Diabat, A., & Simchi-Levi, D. (2012). Sustainable supply chain design: a closed-loop formulation and sensitivity analysis. Production planning & control23(2-3), 120-133.
[2]      Amin, S. H., & Zhang, G. (2012). An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach. Expert systems with applications39(8), 6782-6791.
[3]      Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2017). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega66, 258-277.
[4]      Bai, X., & Liu, Y. (2016). Robust optimization of supply chain network design in fuzzy decision system. Journal of intelligent manufacturing27(6), 1131-1149.
[5]      Brandenburg, M., Govindan, K., Sarkis, J., & Seuring, S. (2014). Quantitative models for sustainable supply chain management: Developments and directions. European journal of operational research233(2), 299-312.
[6]      Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International journal of production economics135(1), 37-49.
[7]      Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research2(6), 429-444.
[8]      Cohen, M. A., & Lee, H. L. (1985). Manufacturing strategy. In The management of productivity and technology in manufacturing (pp. 153-188). Springer, Boston, MA.
[9]       Cohen, M. A., & Lee, H. L. (1988). Strategic analysis of integrated production-distribution systems: Models and methods. Operations research36(2), 216-228.
[10]  Cohen, M. A., & Lee, H. L. (1989). Resource deployment analysis of global manufacturing and distribution networks. Journal of manufacturing and operations management2(2), 81-104.
[11]  Desport, P., Lardeux, F., Lesaint, D., Cairano-Gilfedder, C. D., Liret, A., & Owusu, G. (2017). A combinatorial optimisation approach for closed-loop supply chain inventory planning with deterministic demand. European journal of industrial engineering11(3), 303-327.
[12]  Easwaran, G., & Üster, H. (2010). A closed-loop supply chain network design problem with integrated forward and reverse channel decisions. Iie transactions42(11), 779-792.
[13]  Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design: An overview of classifications, models, solution techniques and applications. Omega45, 92-118.
[14]  Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation research part E: Logistics and transportation review101, 176-200.
[15]  Geoffrion, A. M., & Graves, G. W. (1974). Multicommodity distribution system design by Benders decomposition. Management science20(5), 822-844.
[16]  Govindan, K., & Fattahi, M. (2017). Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain. International journal of production economics183, 680-699.
[17]  Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European journal of operational research263(1), 108-141.
[18]  Gupta, S., & Palsule-Desai, O. D. (2011). Sustainable supply chain management: review and research opportunities. IIMB management review23(4), 234-245.
[19]  Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation research part E: Logistics and transportation review87, 20-52.
[20]  Jiang, D., Li, H., Yang, T., & Li, D. (2016). Genetic algorithm for inventory positioning problem with general acyclic supply chain networks. European journal of industrial engineering10(3), 367-384.
[21]  Keyvanshokooh, E., Ryan, S. M., & Kabir, E. (2016). Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition. European journal of operational research249(1), 76-92.
[22]  [Luthra, S., Govindan, K., Kannan, D., Mangla, S. K., & Garg, C. P. (2017). An integrated framework for sustainable supplier selection and evaluation in supply chains. Journal of cleaner production140, 1686-1698.
[23]  Talluri, S. (2000). A benchmarking method for business-process reengineering and improvement. International journal of flexible manufacturing systems12(4), 291-304.
[24]  Talluri, S., & Baker, R. C. (2002). A multi-phase mathematical programming approach for effective supply chain design. European journal of operational research141(3), 544-558.
[25]  Torabi, S. A., Namdar, J., Hatefi, S. M., & Jolai, F. (2016). An enhanced possibilistic programming approach for reliable closed-loop supply chain network design. International journal of production research54(5), 1358-1387.
[26]  Zhou, G., & Min, H. (2011). Designing a closed-loop supply chain with stochastic product returns: a Genetic Algorithm approach. International journal of logistics systems and management9(4), 397-418.