Akbarpour, M., Torabi, S. A., & Ghavamifar, A. (2020). Designing an integrated pharmaceutical relief chain network under demand uncertainty. Transportation Research Part E: Logistics and Transportation Review, 136, 101867.
Aliabadi, L., Yazdanparast, R., & Nasiri, M. M. (2019). An inventory model for non-instantaneous deteriorating items with credit period and carbon emission sensitive demand: a signomial geometric programming approach. International Journal of Management Science and Engineering Management, 14(2), 124-136.
Biuki, M., Kazemi, A., & Alinezhad, A. (2020). An integrated location-routing-inventory model for sustainable design of a perishable products supply chain network. Journal of Cleaner Production, 120842.
Brusset, X., & Teller, C. (2017). Supply chain capabilities, risks, and resilience. International Journal of Production Economics, 184, 59-68.
Caunhye, A. M., & Cardin, M.-A. (2018). Towards more resilient integrated power grid capacity expansion: A robust optimization approach with operational flexibility. Energy Economics, 72, 20-34.
Chan, F. T., Wang, Z., Goswami, A., Singhania, A., & Tiwari, M. (2020). Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations. International Journal of Production Research, 1-20.
Chen, J., Sohal, A. S., & Prajogo, D. I. (2013). Supply chain operational risk mitigation: a collaborative approach. International Journal of Production Research, 51(7), 2186-2199.
Dellino, G., Laudadio, T., Mari, R., Mastronardi, N., & Meloni, C. (2018). A reliable decision support system for fresh food supply chain management. International Journal of Production Research, 56(4), 1458-1485.
DuHadway, S., Carnovale, S., & Hazen, B. (2019). Understanding risk management for intentional supply chain disruptions: Risk detection, risk mitigation, and risk recovery. Annals of Operations Research, 283(1), 179-198.
Dutta, P., & Shrivastava, H. (2020). The design and planning of an integrated supply chain for perishable products under uncertainties. Journal of Modelling in Management.
Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2019). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research, 283(1-2), 199-224.
Ensafian, H., Yaghoubi, S., & Yazdi, M. M. (2017). Raising quality and safety of platelet transfusion services in a patient-based integrated supply chain under uncertainty. Computers & Chemical Engineering, 106, 355-372.
Fahimnia, B., Jabbarzadeh, A., Ghavamifar, A., & Bell, M. (2017). Supply chain design for efficient and effective blood supply in disasters. International Journal of Production Economics, 183, 700-709.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation research part E: Logistics and transportation review, 101, 176-200.
Fazli-Khalaf, M., Naderi, B., & Mohammadi, M. (2018). Design of a reliable supply chain network with responsiveness considerations under uncertainty: case study of an Iranian tire manufacturer. Journal of Industrial and Systems Engineering, 11(Special issue: 14th International Industrial Engineering Conference), 120-131.
Hasani, A., & Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study. Transportation Research Part E: Logistics and Transportation Review, 87, 20-52.
Hasani, A., Zegordi, S. H., & Nikbakhsh, E. (2012). Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty. International Journal of Production Research, 50(16), 4649-4669.
Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95-105.
Hiassat, A., Diabat, A., & Rahwan, I. (2017). A genetic algorithm approach for location-inventory-routing problem with perishable products. Journal of manufacturing systems, 42, 93-103.
Hosseini-Motlagh, S.-M., Samani, M. R. G., & Homaei, S. (2020). Blood supply chain management: robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1085-1104.
Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68-87.
Imran, M., Salman Habib, M., Hussain, A., Ahmed, N., & M Al-Ahmari, A. (2020). Inventory Routing Problem in Supply Chain of Perishable Products under Cost Uncertainty. Mathematics, 8(3), 382.
Jabbarzadeh, A., Fahimnia, B., & Sabouhi, F. (2018). Resilient and sustainable supply chain design: sustainability analysis under disruption risks. International Journal of Production Research, 56(17), 5945-5968.
Jabbarzadeh, A., Fahimnia, B., Sheu, J.-B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149.
Khalafi, S., Hafezalkotob, A., Mohamaditabar, D., & Sayadi, M. K. (2020). A novel model for a network of a closed-loop supply chain with recycling of returned perishable goods: A case study of dairy industry. Journal of Industrial and Systems Engineering, 12(4), 136-153.
Khalili, S. M., Jolai, F., & Torabi, S. A. (2017). Integrated production–distribution planning in two-echelon systems: a resilience view. International Journal of Production Research, 55(4), 1040-1064.
La Scalia, G., Nasca, A., Corona, O., Settanni, L., & Micale, R. (2017). An innovative shelf life model based on smart logistic unit for an efficient management of the perishable food supply chain. Journal of Food Process Engineering, 40(1), e12311.
Losada, C., Scaparra, M. P., & O’Hanley, J. R. (2012). Optimizing system resilience: a facility protection model with recovery time. European Journal of Operational Research, 217(3), 519-530.
Lücker, F., & Seifert, R. W. (2017). Building up resilience in a pharmaceutical supply chain through inventory, dual sourcing and agility capacity. Omega, 73, 114-124.
Mari, S. I., Lee, Y. H., & Memon, M. S. (2014). Sustainable and resilient supply chain network design under disruption risks. Sustainability, 6(10), 6666-6686.
Marufuzzaman, M., & Ekşioğlu, S. D. (2017). Designing a reliable and dynamic multimodal transportation network for biofuel supply chains. Transportation Science, 51(2), 494-517.
Meena, P., & Sarmah, S. (2013). Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach. Transportation Research Part E: Logistics and Transportation Review, 50, 84-97.
Mulvey, J. M., & Ruszczyński, A. (1995). A new scenario decomposition method for large-scale stochastic optimization. Operations research, 43(3), 477-490.
Namdar, J., Li, X., Sawhney, R., & Pradhan, N. (2018). Supply chain resilience for single and multiple sourcing in the presence of disruption risks. International Journal of Production Research, 56(6), 2339-2360.
Ni, N., Howell, B. J., & Sharkey, T. C. (2018). Modeling the impact of unmet demand in supply chain resiliency planning. Omega, 81, 1-16.
Onggo, B. S., Panadero, J., Corlu, C. G., & Juan, A. A. (2019). Agri-food supply chains with stochastic demands: A multi-period inventory routing problem with perishable products. Simulation Modelling Practice and Theory, 97, 101970.
Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of business logistics, 31(1), 1-21.
Rabbani, M., Yazdanparast, R., & Mobini, M. (2019). An algorithm for performance evaluation of resilience engineering culture based on graph theory and matrix approach. International Journal of System Assurance Engineering and Management, 10(2), 228-241.
Ratick, S., Meacham, B., & Aoyama, Y. (2008). Locating backup facilities to enhance supply chain disaster resilience. Growth and Change, 39(4), 642-666.
Rezaei-Malek, M., Tavakkoli-Moghaddam, R., Zahiri, B., & Bozorgi-Amiri, A. (2016). An interactive approach for designing a robust disaster relief logistics network with perishable commodities. Computers & Industrial Engineering, 94, 201-215.
Rezapour, S., Farahani, R. Z., & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259(3), 1017-1035.
Sahebjamnia, N., Torabi, S. A., & Mansouri, S. A. (2015). Integrated business continuity and disaster recovery planning: Towards organizational resilience. European Journal of Operational Research, 242(1), 261-273.
Sahraeian, R., & Esmaeili, M. (2018). A multi-objective two-echelon capacitated vehicle routing problem for perishable products. Journal of Industrial and Systems Engineering, 11(2), 62-84.
Schmitt, A. J., & Singh, M. (2012). A quantitative analysis of disruption risk in a multi-echelon supply chain. International Journal of Production Economics, 139(1), 22-32.
Sharifi, M., Hosseini-Motlagh, S.-M., Samani, M. R. G., & Kalhor, T. (2020). Novel resilient-sustainable strategies for second-generation biofuel network design considering Neem and Eruca Sativa under hybrid stochastic fuzzy robust approach. Computers & Chemical Engineering, 143, 107073.
Tang, C. S. (2006). Perspectives in supply chain risk management. International journal of production economics, 103(2), 451-488.
Tavana, M., Abtahi, A.-R., Di Caprio, D., Hashemi, R., & Yousefi-Zenouz, R. (2018). An integrated location-inventory-routing humanitarian supply chain network with pre-and post-disaster management considerations. Socio-Economic Planning Sciences, 64, 21-37.
Torabi, S., Baghersad, M., & Mansouri, S. (2015). Resilient supplier selection and order allocation under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 79, 22-48.
Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: a literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508-3526.
Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, 282-305.
Yavari, M., & Zaker, H. (2019). An integrated two-layer network model for designing a resilient green-closed loop supply chain of perishable products under disruption. Journal of Cleaner Production, 230, 198-218.
Yazdanparast, R., Tavakkoli-Moghaddam, R., Heidari, R., & Aliabadi, L. (2018). A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study. Central European Journal of Operations Research, 1-21.
Yu, C.-S., & Li, H.-L. (2000). A robust optimization model for stochastic logistic problems. International journal of production economics, 64(1-3), 385-397.
Zahiri, B., Jula, P., & Tavakkoli-Moghaddam, R. (2018). Design of a pharmaceutical supply chain network under uncertainty considering perishability and substitutability of products. Information Sciences, 423, 257-283.
Zahiri, B., Tavakkoli-Moghaddam, R., Mohammadi, M., & Jula, P. (2014). Multi-objective design of an organ transplant network under uncertainty. Transportation Research Part E: Logistics and Transportation Review, 72, 101-124.
Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109-142.
Zandkarimkhani, S., Mina, H., Biuki, M., & Govindan, K. (2020). A chance constrained fuzzy goal programming approach for perishable pharmaceutical supply chain network design. Annals of Operations Research, 1-28.