Abazari, S.R., Aghsami, A., Rabbani, M., 2021. Prepositioning and distributing relief items in humanitarian logistics with uncertain parameters. Socio-Economic Planning Sciences 74, 100933.
Abdi, A., Abdi, A., Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., 2021. A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty. International Journal of Systems Science: Operations & Logistics 8, 23-40.
Adenso‐Diaz, B., Mena, C., García‐Carbajal, S., Liechty, M., 2012. The impact of supply network characteristics on reliability. Supply Chain Management: An International Journal.
Badri, H., Ghomi, S.F., Hejazi, T.-H., 2017. A two-stage stochastic programming approach for value-based closed-loop supply chain network design. Transportation Research Part E: Logistics and Transportation Review 105, 1-17.
Bank, M., Mazdeh, M., Heydari, M., 2020. Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain. Uncertain Supply Chain Management 8, 77-92.
Dantzig, G.B., 2004. Linear programming under uncertainty. Management Science 50, 1764-1769.
Dehghani, M., Vahdat, V., Amiri, M., Rabiei, E., Salehi, S., 2019. A multi-objective optimization model for a reliable generalized flow network design. Computers & Industrial Engineering 138, 106074.
Diabat, A., Dehghani, E., Jabbarzadeh, A., 2017. Incorporating location and inventory decisions into a supply chain design problem with uncertain demands and lead times. Journal of Manufacturing Systems 43, 139-149.
Dillon, M., Oliveira, F., Abbasi, B., 2017. A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics 187, 27-41.
Faertes, D., 2015. Reliability of supply chains and business continuity management. Procedia Computer Science 55, 1400-1409.
Fathollahi-Fard, A.M., Govindan, K., Hajiaghaei-Keshteli, M., Ahmadi, A., 2019. A green home health care supply chain: New modified simulated annealing algorithms. Journal of Cleaner Production 240, 118200.
Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., Mirjalili, S., 2018. Multi-objective stochastic closed-loop supply chain network design with social considerations. Applied Soft Computing 71, 505-525.
Fatyanosa, T.N., Sihananto, A.N., Alfarisy, G.A.F., Burhan, M.S., Mahmudy, W.F., 2017. Hybrid genetic algorithm and simulated annealing for function optimization. Journal of Information Technology and Computer Science 1, 82-97.
Gholami, F., Paydar, M.M., Hajiaghaei-Keshteli, M., Cheraghalipour, A., 2019. A multi-objective robust supply chain design considering reliability. Journal of Industrial and Production Engineering 36, 385-400.
Goldberg, D.E., Holland, J.H., 1988. Genetic algorithms and machine learning. Machine Learning 3, 5.
Goli, A., Zare, H.K., Tavakkoli‐Moghaddam, R., Sadegheih, A., 2020. Multiobjective fuzzy mathematical model for a financially constrained closed‐loop supply chain with labor employment. Computational Intelligence 36, 4-34.
Goodarzian, F., Hosseini-Nasab, H., 2021. Applying a fuzzy multi-objective model for a production–distribution network design problem by using a novel self-adoptive evolutionary algorithm. International Journal of Systems Science: Operations & Logistics 8, 1-22.
Goodarzian, F., Hosseini-Nasab, H., Fakhrzad, M., 2020. A multi-objective sustainable medicine supply chain network design using a novel hybrid multi-objective metaheuristic algorithm. International Journal of Engineering 33, 1986-1995.
Govindan, K., Jafarian, A., Nourbakhsh, V., 2019. Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics. Computers & Operations Research 110, 220-235.
Ha, C., Jun, H.-B., Ok, C., 2018. A mathematical definition and basic structures for supply chain reliability: A procurement capability perspective. Computers & Industrial Engineering 120, 334-345.
Hamidieh, A., Fazli-Khalaf, M., 2017. A possibilistic reliable and responsive closed loop supply chain network design model under uncertainty. Journal of Advanced Manufacturing Systems 16, 317-338.
Heckmann, I., Comes, T., Nickel, S., 2015. A critical review on supply chain risk–Definition, measure and modeling. Omega 52, 119-132.
Hsu, P.-Y., Angeloudis, P., Aurisicchio, M., 2018. Optimal logistics planning for modular construction using two-stage stochastic programming. Automation in construction 94, 47-61.
Jahani, H., Abbasi, B., Alavifard, F., Talluri, S., 2018. Supply chain network redesign with demand and price uncertainty. International Journal of Production Economics 205, 287-312.
Kamalahmadi, M., Mellat-Parast, M., 2016. Developing a resilient supply chain through supplier flexibility and reliability assessment. International Journal of Production Research 54, 302-321.
Khalifehzadeh, S., Fakhrzad, M.B., Mehrjerdi, Y.Z., Hosseini_Nasab, H., 2019. Two effective metaheuristic algorithms for solving a stochastic optimization model of a multi-echelon supply chain. Applied Soft Computing 76, 545-563.
Li, Y., Guo, H., Wang, L., Fu, J., 2013. A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under E-supply chain environment. The Scientific World Journal 2013.
Maharjan, R., Hanaoka, S., 2017. Warehouse location determination for humanitarian relief distribution in Nepal. Transportation research procedia 25, 1151-1163.
Nosrati, M., Khamseh, A., 2020. Reliability optimization in a four-echelon green closed-loop supply chain network considering stochastic demand and carbon price. Uncertain Supply Chain Management 8, 457-472.
Pasandideh, S.H.R., Niaki, S.T.A., Asadi, K., 2015. Optimizing a bi-objective multi-product multi-period three echelon supply chain network with warehouse reliability. Expert Systems with Applications 42, 2615-2623.
Rahmani, D., Mahoodian, V., 2017. Strategic and operational supply chain network design to reduce carbon emission considering reliability and robustness. Journal of Cleaner Production 149, 607-620.
Sahebjamnia, N., Fathollahi-Fard, A.M., Hajiaghaei-Keshteli, M., 2018. Sustainable tire closed-loop supply chain network design: Hybrid metaheuristic algorithms for large-scale networks. Journal of Cleaner Production 196, 273-296.
Samadi, A., Hajiaghaei-Keshteli, M., Tavakkoli-Moghaddam, R., 2020. Solving a Discounted Closed-Loop Supply Chain Network Design Problem by Recent Metaheuristics, Fuzzy Information and Engineering-2019. Springer, pp. 3-24.
Shone, R., Glazebrook, K., Zografos, K.G., 2021. Applications of stochastic modeling in air traffic management: Methods, challenges and opportunities for solving air traffic problems under uncertainty. European Journal of Operational Research 292, 1-26.
Siddique, N., Adeli, H., 2016. Simulated annealing, its variants and engineering applications. International Journal on Artificial Intelligence Tools 25, 1630001.
Tirkolaee, E.B., Mardani, A., Dashtian, Z., Soltani, M., Weber, G.-W., 2020. A novel hybrid method using fuzzy decision making and multi-objective programming for sustainable-reliable supplier selection in two-echelon supply chain design. Journal of Cleaner Production 250, 119517.
Tolooie, A., Maity, M., Sinha, A.K., 2020. A two-stage stochastic mixed-integer program for reliable supply chain network design under uncertain disruptions and demand. Computers & Industrial Engineering 148, 106722.
Van Laarhoven, P.J., Aarts, E.H., 1987. Simulated annealing, Simulated annealing: Theory and applications. Springer, pp. 7-15.
Wang, M., Wu, J., Kafa, N., Klibi, W., 2020. Carbon emission-compliance green location-inventory problem with demand and carbon price uncertainties. Transportation Research Part E: Logistics and Transportation Review 142, 102038.
Whitley, D., 1994. A genetic algorithm tutorial. Statistics and computing 4, 65-85.
Yildiz, H., Yoon, J., Talluri, S., Ho, W., 2016. Reliable supply chain network design. Decision Sciences 47, 661-698.
Yolmeh, A., Saif, U., 2021. Closed-loop supply chain network design integrated with assembly and disassembly line balancing under uncertainty: an enhanced decomposition approach. International Journal of Production Research 59, 2690-2707.
Zhou, X., Zhang, H., Qiu, R., Lv, M., Xiang, C., Long, Y., Liang, Y., 2019. A two-stage stochastic programming model for the optimal planning of a coal-to-liquids supply chain under demand uncertainty. Journal of Cleaner Production 228, 10-28.