Pishvaee, M.S., & Rabbani, M., (2011). A graph theoretic-based heuristic algorithm for responsive supply chain network design with direct and indirect shipment. Advances in Engineering Software, 42(3), 57-63.
 Morganti, E., & Gonzalez-Feliu, J., (2015). City logistics for perishable products. The case of the Parma's Food Hub. Case Studies on Transport Policy, 3(2), 120-128.
 Khodaparasti, S., Bruni, M.E., Beraldi, P., Maleki, H.R., & Jahedi, S., (2018). A multi-period location-allocation model for nursing home network planning under uncertainty. Operations Research for Health Care.
 Kovačić, D., Hontoria, E., Ros-McDonnell, L., & Bogataj, M., (2015). Location and lead-time perturbations in multi-level assembly systems of perishable goods in Spanish baby food logistics. Central European journal of operations research, 23(3), 607-623.
 Jalalifar, S., Rasi Ehtehsma, R., Mohtashami, A. (2021). Design a Fuzzy Goal Progr amming Model for Optimizing the Cost and Distance of Vehicles in the Four-Echelon Closed-Loop Supply Chain by Using Ant Colony Algorithm. Modern Researches in Decision Making, 6(1), pp.148-169.
 Kodadaian, D., Radfar, R., Toloei, A. (2020). A Multi-Objective Green Supply Chain: Multi-Product Model Considering Uncertainty. Modern Researches in Decision Making, 5(3), 1-28.
 Pishvaee, M. S., & Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming. Applied Mathematical Modelling, 36(8), 3433-3446.
 Simchi-levi D. & Kaminsky,Ph. (2006). Effects of supply chain management practices, integration and competition capability on performance,(11)3, 1359-8546
 Dai, Z., Aqlan, F., Zheng, X., & Gao, K., (2018). A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints. Computers & Industrial Engineering.
 Rafie-Majd, Z., Pasandideh, S.H.R., & Naderi, B., (2018). Modelling and solving the integrated inventory-location-routing problem in a multi-period and multi-perishable product supply chain with uncertainty: Lagrangian relaxation algorithm. Computers & Chemical Engineering, 109, 9-22.
 Li, L., Dababneh, F., & Zhao, J., (2018). Cost-effective supply chain for electric vehicle battery remanufacturing. Applied energy, 226, 277-286.
 Saif-Eddine, A.S., El-Beheiry, M.M., & El-Kharbotly, A.K. (2019). An improved genetic algorithm for optimizing total supply chain cost in inventory location routing problem. Ain Shams Engineering Journal, 2019, 10(1), 63-76.
 Oh, J., & Jeong, B. (2019). Tactical supply planning in smart manufacturing supply chain. Robotics and Computer-Integrated Manufacturing, 2019, 55, 217-233.
 Pellegrino, R., Costantino, N., & Tauro, D., (2019). Supply Chain Finance: A supply chain-oriented perspective to mitigate commodity risk and pricing volatility. Journal of Purchasing and Supply Management, 2019, 25(2), 118-133.
 Moretto, A., Grassi, L., Caniato, F., Giorgino, M., & Ronchi, S., (2019). Supply chain finance: From traditional to supply chain credit rating. Journal of Purchasing and Supply Management, 25(2), 197-217.
 Cole, R., & Aitken, J., (2019). The role of intermediaries in establishing a sustainable supply chain. Journal of Purchasing and Supply Management, 26(2).
 Reimann, M., Xiong, Y., & Zhou, Y., (2019). Managing a closed-loop supply chain with process innovation for remanufacturing. European Journal of Operational Research, 276(2), 510-518.
 Asim, Z., Jalil, S.A., & Javaid, S., (2019). An uncertain model for integrated production-transportation closed-loop supply chain network with cost reliability. Sustainable Production and Consumption, 17, 298-310.
 Sun, S., & Wang, X., (2019). Promoting traceability for food supply chain with certification. Journal of Cleaner Production, 217, 658-665.
 Wang, X., Guo, H., Yan, R., & Wang, X., (2018). Achieving optimal performance of supply chain under cost information asymmetry. Applied Mathematical Modelling, 53, 523-539.
 Wu, T., Zhang, L.G., & Ge, T., (2018). Managing financing risk in capacity investment under green supply chain competition. Technological Forecasting and Social Change, 143, 37-44.