[1] Kakabadse, A., & Kakabadse, N. (2002). Trends in outsourcing:: Contrasting USA and Europe. European management journal, 20(2), 189-198.
[2] Ferguson M, Toktay LB. The effect of competition on recovery strategies. Prod Oper Manag 2006;15(3):351–68
[3] Martin P, Guide VDR Jr, Craighead CW. Supply chain sourcing in remanufacturing operations: an empirical investigation to remake versus buy. Decis Sci 2010;41(2):301–21
[4] Meade L, Sarkis J. A conceptual model for selecting and evaluating third party reverse logistics providers. Supply Chain Manag 2002;7(5):283–95.
[5] Daugherty PJ, Droge C. Organizational structure in divisionalized manufacturers: the potential for outsourcing logistical services. Int J Phys Distrib Logist Manag 1997;27(5/6):337–49. De Brito MP, Dekker R. Rev
[6] Insigna RC, Werle MJ. Linking outsourcing to business strategy. Acad Manag Exec 2000;14(4):58–70
[7] Boyson S, Corse T, Dresner D, Rabinovich E. Managing effective third party logistics relationships: what does it take? J Bus Logist 1999;20:73–100.
[8] Arnold U. New dimensions of outsourcing: a combination of transaction cost economics and the core competencies concept. Eur J Purch Supply Manag2000;6(1):23–9.
[9] Wu F, Li HZ, Chu LK, Sculli D. An outsourcing model for sustaining long-term performance. Int J Prod Res 2005;43(12):2513–35.
[10] Serrato MA, Ryan SM, Gaytan J. A Markov decision model to evaluate outsourcing in reverse logistics. Int J Prod Res 2007;45(18–19):4289–315
[11] Ko HJ, Evans GW. A genetic algorithm-based heuristic for the dynamic integrated forward/reverse logistics network for 3PLs. Comput Oper Res 2007;34(2):346–66
[12] Pagell M, Wu Z, Murthy NN. The supply chain implications of recycling. Bus Horiz 2007;50:133–43.
[13] Kannan D, Diabat A, Shankar KM. Analyzing the drivers of end-of-life tire management using interpretive structural modeling (ISM). Int J Adv Manuf Technol 2012;72(9–12):1603–14
[14] Huscroft JR, Hazen BT, Hall DJ, Hanna JB. Task-technology fit for reverse logistics performance. Int J Logist Manag 2012;24(2):230–46
[15] Saurabh Agrawal , Rajesh K. Singh, Qasim Murtaza, A literature review and perspectives in reverse logistics, Resources, Conservation and Recycling 97 (2015) 76–92.
[16] Krumwiede DW, Sheu C. A model for reverse logistics entry by third party providers. Omega 2002;30(5):325–33.
[17] Ordoobadi SM. Outsourcing reverse logistics and remanufacturing functions: a conceptual strategic model. Manag Res News 2009;32(9):831–45.
[18] Bernon M, Rossi S, Cullen J. Retail reverse logistics: a call and grounding framework for research. Int J Phys Distrib Logist Manag 2011;41(5):484–510
[19] Mafakheri F, Nasiri F. Revenue sharing coordination in reverse logistics. J Clean Prod2013;59:185–96
[20] Bottani E, Rizzi A. A fuzzy TOPSIS methodology to support outsourcing of logistics services. Supply Chain Manag 2006;11(4):294–308.
[21] Poles, R., & Cheong, F. (2009). A system dynamics model for reducing uncertainty in remanufacturing systems. PACIS 2009 Proceedings, 24.
[22] Gicquel, C., Kedad-Sidhoum, S., & Quadri, D. (2016, June). Remanufacturing planning under uncertainty: a two-stage stochastic programming approach. In International Conference on Informations Systems, Logistics and Supply chain ILS2016.
[23] Lee, D.-H., Dong, M., 2009. Dynamic network design for reverse logistics operations under uncertainty. Transp. Res. Part E Logist. Transp. Rev. 45, 61e71.
[24] El-Sayed, M., Afia, N., El-Kharbotly, A., 2010. A stochastic model for forward-reverse logistics network design under risk. Comput. Ind. Eng. 58, 423e431.
[25] Chu, L.K., Shi, Y., Lin, S., Sculli, D., Ni, J., 2010. Fuzzy chance-constrained programming model for a multi-echelon reverse logistics network for household appliances. J. Oper. Res. Soc. 61, 551e560.
[26] Fonseca, M.C., García-Sanchez, A., Ortega-Mier, M., Saldanha-Da-Gama, F., 2010.A stochastic bi-objective location model for strategic reverse logistics. Top 18,158e184.
[27] Kenne, J. P., Dejax, P., & Gharbi, A. (2012). Production planning of a hybrid manufacturing–remanufacturing system under uncertainty within a closed-loop supply chain. International Journal of Production Economics, 135(1), 81-93.
[28] Ramezani, M., Bashiri, M., Tavakkoli-Moghaddam, R., 2013. A new multi-objective stochastic model for a forward/reverse logistic network design with responsiveness and quality level. Appl. Math. Model. 37, 328e344
[29] De Rosa, V., Gebhard, M., Hartmann, E., Wollenweber, J., 2013. Robust sustainable bi-directional logistics network design under uncertainty. Int. J. Prod. Econ. 145, 184e198
[30] Chen, Y. J., & Liu, D. B. (2013). An uncertain programming model for manufacturing/remanufacturing hybrid system in reverse logistics environment. In Applied Mechanics and Materials (Vol. 288, pp. 251-255). Trans Tech Publications.
[31] Roghanian, E., Pazhoheshfar, P., 2014. An optimization model for reverse logistics network under stochastic environment by using genetic algorithm. J. Manuf. Syst. 33, 348e356
[32] Soleimani, H., Govindan, K., 2014. Reverse logistics network design and planning utilizing conditional value at risk. Eur. J. Oper. Res. 237, 487e497
[33] Govindan, K., Paam, P., Abtahi, A.-R., 2016b. A fuzzy multi-objective optimization model for sustainable reverse logistics network design. Ecol. Indic. 67, 753e768
[34] Soleimani, H., Seyyed-Esfahani, M., Shirazi, M.A., 2016. A new multi-criteria scenario- based solution approach for stochastic forward/reverse supply chain network design. Ann. Oper. Res. 242, 399e421
[35] Feito-Cespon, M., Sarache, W., Piedra-Jimenez, F., Cespon-Castro, R., 2017. Redesign of a sustainable reverse supply chain under uncertainty: a case study. J. Clean. Prod. 151, 206e217.
[36] Soleimani, H., Govindan, K., Saghafi, H., Jafari, H., 2017. Fuzzy multi-objective sustainable and green closed-loop supply chain network design. Comput. Ind. Eng. 109, 191e203
[37] Yu, H., & Solvang, W. D. (2017). A carbon-constrained stochastic optimization model with augmented multi-criteria scenario-based risk-averse solution for reverse logistics network design under uncertainty. Journal of cleaner production, 164, 1248-1267.