Document Type : Research Paper


1 Department of Industrial Engineering, Islamic Azad University, Sanandaj Branch, Sanandaj, Kurdistan, Iran

2 Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

3 Department of Industrial Management, Faculty of Management and Accounting, Shahid Beheshti University, G.C., Tehran, Iran

4 Department of Statistics and Actuarial Science, University of Waterloo, Ontario, Canada


This paper formulates a competitive supply chain network throughout a mixed integer linear programming problem, considering demand uncertainty and retailers risk averseness. That is, makes the model more realistic in comparison with the others. Employed conditional value at risk method through the data-driven approach, makes the model to be convex and sensitive to the risk averseness level. Finally, the model outputs and its results are illustrated through a numerical example.


Main Subjects

[1]   Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, 37(4), 668-683.
[2]   Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262-269.
[3]   Jamshidi, R., Ghomi, S. F., & Karimi, B. (2012). Multi-objective green supply chain  optimization with a new hybrid memetic algorithm  using  the  Taguchi  method. Scientia Iranica, 19(6), 1876-1886.
[4]   Fahimnia, B., Farahani, R. Z., Marian, R., & Luong, L. (2013). A review and critique on integrated production–distribution planning models and techniques. Journal of Manufacturing Systems, 32(1), 1-19.
[5]   Lou, C. X., & Dai, W. (2012, September). Robust Supply Chain Services System through Optimization Modeling for Enterprises. In 2012 15th International Conference on Network- Based Information Systems (pp. 518-523). IEEE.
[6]   Baghalian, A., Rezapour, S., & Farahani, R. Z. (2013). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research,227(1), 199-215.
[7]   Fahimnia, B., Farahani, R. Z., & Sarkis, J. (2013). Integrated aggregate supply chain planning using memetic algorithm–A performance analysis case study. International Journal of Production Research, 51(18), 5354-5373.
[8]   Liu, S., & Papageorgiou, L. G. (2013). Multiobjective optimisation of production, distribution and capacity planning of global supply chains in the process industry. Omega, 41(2), 369-382.
[9]   Hashim, M., Nazim, M., & Nadeem, A. H. (2013). Production-Distribution Planning in Supply Chain Management Under Fuzzy Environment for Large-Scale Hydropower Construction Projects. In Proceedings of the Sixth International Conference on Management Science and Engineering Management (pp. 559-576). Springer London.
[10]    Xiao, T., & Yang, D. (2008). Price and service competition of supply chains with risk-averse retailers under demand uncertainty. International Journal of Production Economics, 114(1), 187- 200.
[11]    Rezapour, S., & Farahani, R. Z. (2014). Supply chain network design under oligopolistic price and service level competition with foresight. Computers & Industrial Engineering, 72, 129-142.
[12]    Farahani, R. Z., Rezapour, S., Drezner, T., & Fallah, S. (2014). Competitive supply chain network design:  An  overview  of  classifications,  models,  solution  techniques  and applications. Omega, 45, 92-118.
[13]    Chen, H. K., Chou, H. W., & Chiu, Y. C. (2007). On the modeling and solution algorithm for the reverse logistics recycling flow equilibrium problem.Transportation Research Part C: Emerging Technologies, 15(4), 218-234.
[14]    Majumder, P., & Srinivasan, A. (2008). Leadership and competition in  network  supply  chains. Management Science, 54(6), 1189-1204.
[15]    Liu, Z., & Nagurney, A. (2012). Multiperiod competitive supply chain networks with inventorying and a transportation network equilibrium reformulation. Optimization and Engineering, 13(3), 471-503.
[16]    Samuelson, P. A. (1952). Spatial price equilibrium and linear programming. The American economic review, 42(3), 283-303.
[17]    Gui-tao, Z., Hao, S., & Jin-song, H. (2014, June). Research on supply chain network equilibrium problem with multi-type suppliers. In Service Systems and Service Management (ICSSSM), 2014 11th International Conference on(pp. 1-5). IEEE.
[18]    Dong, J., Zhang, D., & Nagurney, A. (2004). A supply chain network equilibrium model with random demands. European Journal of Operational Research, 156(1), 194-212.
[19]    Nagurney, A.,  Dong,  J.,  &  Zhang,  D.  (2002).  A  supply  chain  network  equilibrium  model. Transportation Research Part E: Logistics and Transportation Review, 38(5), 281-303.
[20]    Nagurney, A., & Dong, J. (2002). Supernetworks: decision-making for the information age. Elgar, Edward Publishing, Incorporated.
[21]    Nagurney, A., Cruz, J., Dong, J., & Zhang, D. (2005). Supply chain networks, electronic commerce, and supply side and  demand  side  risk.European  Journal  of  Operational  Research, 164(1), 120-142.
[22]    Dong, J., Zhang, D., Yan, H., & Nagurney, A. (2005). Multitiered supply chain networks: multicriteria decision—making under uncertainty. Annals of Operations Research, 135(1), 155- 178.
[23]    Hamdouch, Y. (2011). Multi-period supply chain network equilibrium with capacity constraints and purchasing strategies. Transportation Research Part C: Emerging Technologies, 19(5), 803- 820.
[24]    Qiang, Q., Ke, K., Anderson, T., & Dong, J. (2013). The closed-loop supply chain network  with competition, distribution channel investment, and uncertainties. Omega, 41(2), 186-194.
[25]    Bertsimas, D., & Brown, D. B. (2009). Constructing uncertainty sets for robust linear optimization. Operations research, 57(6), 1483-1495.