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Amiri, A. (2006). Designing a distribution network in a supply chain system: Formulation and efficient solution procedure. European Journal of Operational Research, 171 (2), 567-576.
Azaron, A., Brown, K. N., Tarim, S. A., & Modarres, M. (2008). A multi-objective stochastic programming approach for supply chain design considering risk. International Journal of Production Economics, 116 (1), 129-138.
Babazadeh, R., Razmi, J., Pishvaee, M. S., & Rabbani, M. (2017). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega, 66, 258-277.
Ben-Tal, A., & Nemirovski, A. (1998). Robust solutions of uncertain linear programs. Operations research letters, 25(1), 1-13.
Ben-Tal, A., & Nemirovski, A. (2000). Robust solutions of linear programming problems contaminated with uncertain data. Mathematical programming, 88(3), 411-424.
Ben-Tal, A., & Nemirovski, A. (2009). Selected topics in robust convex optimization. Mathematical Programming, 112(1), 125-158.
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations research, 52(1), 35-53.
Chouinard, M., D’Amours, S., & Aït-Kadi, D. (2008). A stochastic
programming approach for designing supply loops. International Journal of Production Economics, 113(2), 657-677
Cruz-Rivera, R., & Ertel, J. (2009). Reverse logistics network design for the
collection of end-of-life vehicles in Mexico. European Journal of Operational Research, 196(3), 930-939.
Dubois, D., & Prade, H. (1987). The mean value of a fuzzy number. Fuzzy sets and systems, 24(3), 279-300.
El-Sayed, M., Afia, N., & El-Kharbotly, A. (2010). A stochastic model for forward–reverse logistics network design under risk. Computers & Industrial Engineering, 58(3), 423-431.
Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2017). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets and Systems. 1(3), 131-160.
Fleischmann, M., Beullens, P., BLOEMHOF‐RUWAARD, J. M., & Wassenhove, L. N. (2001). The impact of product recovery on logistics network design. Production and operations management, 10(2), 156-173.
Gaur, J., Amini, M., & Rao, A. K. (2017). Closed-loop supply chain configuration for new and reconditioned products: An integrated optimization model. Omega, 66, 212-223.
Govindan, K., Soleimani, H., & Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. European Journal of Operational Research, 240(3), 603-626.
Hasani, A., & Hosseini, S.M.H., (2015). A Comprehensive Robust Biobjective Model and a Memetic Solution Algorithm for Designing Reverse Supply. Journal of Indusrial Mangement Perspective, 16, 31-54 (In Persion).
Hatefi, S. M., & Jolai, F. (2014). Robust and reliable forward–reverse
logistics network design under demand uncertainty and facility disruptions. Applied Mathematical Modelling, 38(9), 2630-2647.
Inuiguchi, M., & Ramık, J. (2000). Possibilistic linear programming: a brief review of fuzzy mathematical programming and a comparison with stochastic programming in portfolio selection problem. Fuzzy sets and systems, 111(1), 3-28.
Inuiguchi, M., & Sakawa, M. (1998). Robust optimization under softness in a fuzzy linear programming problem. International Journal of Approximate Reasoning, 18(1-2), 21-34.
Jayaraman, V., Patterson, R. A., & Rolland, E. (1999). The design of reverse distribution networks: Models and solution procedures. European journal of operational research, 150(1), 128-149.
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 Research, 249(1), 76-92.
Klibi, W., & Martel, A. (2012). Scenario-based supply chain network risk modeling. European Journal of Operational Research, 223(3), 644-658.
Ko, H. J., & Evans, G. W. (2007). A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Computers & Operations Research, 34(2), 346-366.
Lee, D. H., & Dong, M. (2009). Dynamic network design for reverse
logistics operations under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 45(1), 61-71.
Liu, B., & Iwamura, K. (1998). Chance constrained programming with fuzzy parameters. Fuzzy sets and systems, 94(2), 227-237.
Liu, B., & Liu, Y. K. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE transactions on Fuzzy Systems, 10(4), 445-450.
Melo, M. T., Nickel, S., & Saldanha-Da-Gama, F. (2009). Facility location and supply chain management–A review. European journal of operational research, 196(2), 401-412
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
Min, H., Ko, H. J., & Ko, C. S. (2006). A genetic algorithm approach to developing the multi-echelon reverse logistics network for product returns. Omega, 34(1), 56-69.
Mousazadeh, M., Torabi, S. A., & Pishvaee, M. S. (2014). Green and reverse logistics management under fuzziness in Supply Chain Management Under Fuzziness (pp. 607-637). Springer Berlin Heidelberg.
Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy sets and systems, 157(1), 74-97.
Mulvey, J. M., Vanderbei, R. J., & Zenios, S. A. (1995). Robust optimization of large-scale systems. Operations research, 43(2), 264-281.
Nurjanni, K. P., Carvalho, M. S., & Costa, L. (2017). Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model. International Journal of Production Economics, 183, 421-432.
Paksoy, T., Pehlivan, N. Y., & Özceylan, E. (2012). Application of fuzzy optimization to a supply chain network design: a case study of an edible vegetable oils manufacturer. Applied Mathematical Modelling, 36(6), 2762-2776.
Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
Pishvaee, M. S., Razmi, J., & Torabi, S. A. (2012a). Robust possibilistic programming for socially responsible supply chain network design: A new approach. Fuzzy sets and systems, 206, 1-20.
Pishvaee, M. S., Torabi, S. A., & Razmi, J. (2012b). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, 62(2), 624-632.
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Qin, Z., & Ji, X. (2010). Logistics network design for product recovery in fuzzy environment. European Journal of Operational Research, 202(2), 479-490.
Ramezani, M., Bashiri, M., & Tavakkoli-Moghaddam, R. (2013). A robust design for a closed-loop supply chain network under an uncertain environment. The International Journal of Advanced Manufacturing Technology, 66(5-8), 825-843.
Sahinidis, N. V. (2004). Optimization under uncertainty: state-of-the-art and opportunities. Computers & Chemical Engineering, 28(6), 971-983.
Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations research, 21(5), 1154-1157.
Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.
Üster, H., Easwaran, G., Akçali, E., & Çetinkaya, S. (2007). Benders decomposition with alternative multiple cuts for a multi‐product closed‐loop supply chain network design model. Naval Research Logistics (NRL), 54(8), 890-907.
Validi, S., Bhattacharya, A., & Byrne, P. J. (2015). A solution method for a two-layer sustainable supply chain distribution model. Computers & Operations Research, 54, 204-217.
Winkler, H. (2011). Closed-loop production systems—A sustainable supply
chain approach. CIRP Journal of Manufacturing Science and Technology, 4(3), 243- 246.
Xu, J., & Zhou, X. (2013). Approximation based fuzzy multi-objective models with expected objectives and chance constraints: Application to earth-rock work allocation. Information Sciences, 238, 75-95.
Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistic problems. International journal of production economics, 64(1), 385-397.
Zeballos, L. J., Méndez, C. A., Barbosa-Povoa, A. P., & Novais, A. Q. (2014). Multi-period design and planning of closed-loop supply chains with uncertain supply and demand. Computers & Chemical Engineering, 66, 151-164.
Zhalechian, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Mohammadi, M.
(2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E: Logistics and Transportation Review, 89, 182-214.
Zhu, H., & Zhang, J. (2009, November). A credibility-based fuzzy programming model for APP problem. In Artificial Intelligence and Computational Intelligence, 2009. AICI'09. International Conference on (Vol. 1, pp. 455-459). IEEE.
Zohal, M., & Soleimani, H. (2016). Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry. Journal of Cleaner Production, 133, 314-337.