In the atmosphere of sustainable development and low carbon, the design of supply chain network closely related to logistics industry, is becoming more and more important. The traditional supply chain network no longer meets the need of social, economy and environment. After studying the literature from 2011 to 2019, many focused on green and sustainable supply chain network design under uncertain conditions. This paper intends to account for developments in the literature on supply chain management, and highlights both the challenges and opportunities, and more importantly, offers useful insights on the development in the future.

Xiaoning Zhu, Ziqian Zhao. The Construction Path of Rural Ecological Civilization Based on "Beautiful Countryside". Academic Journal of Humanities & Social Sciences (2019) Vol. 2, Issue 6: 1-8. https://doi.org/10.25236/AJHSS.2019.020601.

[1] K. Govindan, A. Jafarian and V. Nourbakhsh (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & Operations Research, vol.62, p.112-130.
[2] H. Badri, S.M.T.F. Ghomi and T.H. Hejazi (2017). A two-stage stochastic programming approach for value-based closed-loop supply chain network design. Transportation Research Part E-Logistics and Transportation Review, vol.105, p.1-17.
[3] G. Brundtland (1987). The Brundtland Report. World commission on environment and development.
[4] J. Elkington and I. Rowlands (1999). Cannibals with forks: the triple bottom line of 21stcentury business. Alternatives Journal, p.25- 42.
[5] K. Devika, A. Jafarian and V. Nourbakhsh (2014). Designing a sustainable closed-loop supply chain network based on triple bottom line approach: a comparison of metaheuristics hybridization techniques. European Journal of Operation Research, vol.235, no.3, p.594-615.
[6] K. Govindan, H. Soleimani and D. Kannan (2015b). Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. European Journal of Operational Research, vol.240, no.3, p.603-626.
[7] L. White and G.J Lee (2009). Operational research and sustainable development: tackling the social dimension. European Journal of Operational Research, vol.193, no.3, pp. 683-692.
[8] P.R., Kleindorfer, K. Singhal and L.N. Van Wassenhove (2005). Sustainable operations management. Production and Operations Management, vol.14, no.4, p.482–492.
[9] D. Yue, M.A. Kim and F. You (2013). Design of sustainable product systems and supply chains with life cycle optimization based on functional unit: general modeling framework, mixed-integer nonlinear programming algorithms and case study on hydrocarbon biofuels. ACS Sustaunable Chemistry & Enginering, vol.1, no.8, p.1003-1014.
[10] B. Mota, M.I. Gomes, A. Carvalho and A.P.B. Povoa (2015). Towards supply chain sustainability: economic, environmental and social design and planning. Journal of Cleaner Production, vol.105, p.14-27.
[11] M. Zhalechian, R. Tavakkoli-Moghaddam, B. Zahiri and M. Mohammadi (2016). Sustainable design of a closed-loop location-routing-inventory supply chain network under mixed uncertainty. Transportation Research Part E-Logistics and Transportation Review, vol.89, p.182-214.
[12] H. Soleimani, K. Govindan, H. Saghafi and H. Jafari (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, vol.109, p.191–203.
[13] B. Zahiri, J. Zhuang and M. Mohammadi (2017). Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transportation Research Part E-Logistics and Transportation Review, vol.103, p.109-142.
[14] M. Sherafati, M. Bashiri, R. Tavakkoli-Moghaddam and M.S. Pishvaee (2019). Supply chain network design considering sustainable development paradigm: A case study in cable industry. Journal of Cleaner Production, vol.234, p.366-380.
[15] M. Eskandarpour, P. Dejax, J. Miemczyk and O. Peton (2015). Sustainable supply chain network design: an optimization-oriented review. Omega-International Journal of Management Science, vol.54, p.11-32.
[16] F. You and B. Wang (2011). Life cycle optimization of biomass-to-liquid supply chains with distributed centralized processing networks. Industrial & Engineering Chemistry Research, vol.50, no.17, p.10102-10127.
[17] T. Pinto-Varela, A.P.F.D. Barbosa-Povoa and A.Q. Novais (2011). Bi-objective optimization approach to the design and planning of supply chains: economic versus environmental performances. Computers & Chemical Engineering, vol.35, no.8, p.1454-1468.
[18] M.S. Pishvaee and J. Razmi (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Model, vol.36. no.8, p.3433-3446.
[19] M.S. Pishvaee and J. Razmi and S.A. Torabi (2012a). Robust possibilistic programming for socially responsible supply chain network design: a new approach. Fuzzy Sets Systems, vol.206, p.1-20.
[20] M.S. Pishvaee, S.A. Torabi and J. Razmi (2012b). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering, vol.62, no.2, p.624-632.
[21] P.N.K. Phuc, F.Y. Vincent and S.Y (2013). Chou. Optimizing the fuzzy closed-loop supply chain for electrical and electronic equipments. International Journal of Fuzzy Systems, vol.15, no.1, p.9-21.
[22] M. Pishvaee, J. Razmi and S. Torabi (2014). An accelerated benders decomposition algorithm for sustainable supply chain network design under uncertainty: a case study of medical needle and syringe supply chain. Transportation Research Part E-Logistics and Transportation Review, vol.67, p.14-38.
[23] M. Talaei, B.F. Moghaddam, M.S. Pishvaee, A. Bozorgi-Amiri and S. Gholamnejad (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, vol.113, p.662-673.
[24] Y.C. Tsao, Q. Zhang and T.H. Chen (2016). Multi-item distribution network design problems under volume discount on transportation cost. International Journal of Production Research, vol.54, no.2, p.426-443.
[25] H. Soleimani, K. Govindan, H. Saghafi and H. Jafari (2017). Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Computers & Industrial Engineering, vol.109, p.191-203.
[26] M. Feito-Cespon, W. Sarache, F. Piedra-Jimenez and R. Cespon-Castro (2017). Redesign of a sustainable reverse supply chain under uncertainty: a case study. Journal of Cleaner Production, vol.151, p.206-217.
[27] B. Zahiri, J. Zhuang, M. Mohammadi (2017). Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transportation Research Part E-Logistics and Transportation Review, vol.103, p.109-142.
[28] C. Arampantzi and I. Minis (2017). A new model for designing sustainable supply chain networks and its application to a global manufacturer. Journal of Cleaner Production, vol, 156, p.276-292.
[29] R. Babazadeh, J. Razmi, M.S. Pishvaee and M. Rabbani (2017a). A sustainable second-generation biodiesel supply chain network design problem under risk. Omega-international Journal of Management Science, vol.66, p.258-277.
[30] R. Babazadeh, J. Razmi, M. Rabbani and M.S. Pishvaee (2017b). An integrated data envelopment analysis-mathematical programming approach to strategic biodiesel supply chain network design problem. Journal of Cleaner Production, vol.147, p.694-707.
[31] K. Govindan, A. Jafarian and V. Nourbakhsh (2018). Designing a sustainable supply chain network integrated with vehicle routing: a comparison of hybrid swarm intelligence metaheuristics. Computer & Operations Research, vol.110, p.220-235.
[32] H. Ghaderi, A. Moini and M.S. Pishvaee (2018). A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design. Journal of Cleaner Production, vol.179, p.368-406.
[33] M. Rahimi and V. Ghezavati (2018). Sustainable multi-period reverse logistics network design and planning under uncertainty utilizing conditional value at risk (cvar) for recycling construction and demolition waste. Journal of Cleaner Production, vol.172, p.1567-1581.
[34] M. Fattahi and K. Govindan (2018). A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: a real-life case study. Transportation Research Part E-Logistics and Transportation Review, vol.118, p.534-567.
[35] N. Sahebjamnia, A.M.F. Fard and M. Hajiaghaei-Keshteli (2018). Sustainable tire closed-loop supply chain network design: hybrid metaheuristic algorithms for largescale networks. Journal of Cleaner Production, vol.196, p.273-296.
[36] B. Mota, M.I. Gomes, A. Carvalho and A.P. Barbosa-Povoa (2018). Sustainable supply chains: an integrated modeling approach under uncertainty. Omega-international Journal of Management Science, vol.77, p.32-57.
[37] H. Allaoui, Y. Guo, A. Choudhary and J. Bloemhof (2018). Sustainable agro-food supply chain design using two-stage hybrid multi-objective decision-making approach. Computer & Operations Research, vol.89, p.369-384.
[38] M. Rahimi, V. Ghezavati and F. Asadi (2019). A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty. Computers & Industrial Engineering, vol.130, p.430-449.
[39] M. Sherafati, M. Bashiri, R. Tavakkoli-Moghaddam and M.S. Pishvaee (2019). Supply chain network design considering sustainable development paradigm: A case study in cable industry. Journal of Cleaner Production, vol.234, p.366-380.
[40] C.L. Chen, T.W. Yuan and W.C. Lee (2007). Multi-criteria fuzzy optimization for locating warehouses and distribution centers in a supply chain network. Journal of the Chinese Institute of Chemical Engineers, vol.38, no.5, p.393–407.
[41] D. Petrovic, Y. Xie, K. Burnham and R. Petrovic (2008). Coordinated control of distribution supply chains in the presence of fuzzy customer demand. European Journal of Operational Research, vol.185, no.1, p.146–158.
[42] T. Efendigil, S. Önüt and C. Kahraman (2009). A decision support system for demand forecasting with artificial neural networks and neuro-fuzzy models: A comparative analysis. Expert Systems with Applications, vol.36, no.3, p.6697–6707.
[43] H. Yu and W.D. Solvang (2018). Incorporating flexible capacity in the planning of a multi-product multi-echelon sustainable reverse logistics network under uncertainty. Journal of Cleaner Production, vol, 198, p.285-303.