Developing a Two-stage Robust Stochastic Model for Designing a Resilient Blood Supply Chain Considering Earthquake Disturbances and Infectious Diseases

References

Abolghasemi, H., Radfar, M. H., Tabatabaee, M., Hosseini-Divkolayee, N. S., & Burkle Jr, F. M. (2008). Revisiting blood transfusion preparedness: Experience from the Bam Earthquake. Prehosp Disaster Med, 23(5), 391-4.

Aghezzaf, E. H., Sitompul, C., & Najid, N. M. (2010). Models for robust tactical planning in multi-stage production systems with uncertain demands. Computers & Operations Research, 37(5), 880-889.

Aghyani, M., Jabbarzadeh, A., Sadjadi, J. (2015). A robust optimization model for designing blood supply chain in crisis considering reliability. Journal of Quality Management & Engineering, 5 (2). 85-96.

Amiri, M., Hosseini Dehshiri, S., Yousefi Hanoomarvar, A. (2018). Determining the Optimal Combination of LARG Supply Chain Strategies Using SWOT Analysis, Multi-criteria Decision-making Techniques and Game Theory. Industrial Management Journal, 10(2), 221-246. (in Persian)

Armaghan, N., & Pazani, N. Y. (2019). A Model For Designing A Blood Supply Chain Network To Earthquake Disasters (Case Study: Tehran City). International Journal for Quality Research, 13(3), 605-624.

Beigi, S., Yaghoubi, H., Karimi, H. (2020). Optimal Allocation of City Districts to Emergency Resettlement Sites, Hospitals, and Clinics after the Earthquake (Case Study: Bojnord City). Industrial Management Journal, 12(1), 82-110. (in Persian)

Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.

Bozorgi, A., Kazemi, N., Baderi, Z. (2015). A Reliable multi-objective location-allocation model for blood supply systems under disruptions. Advances in Mathematical Modeling, 4(2), 1-25. (in Persian)

Caram‐Deelder, C., Kreuger, A. L., Jacobse, J., van der Bom, J. G., & Middelburg, R. A. (2016). Effect of platelet storage time on platelet measurements: a systematic review and meta‐analyses. Vox sanguinis, 111(4), 374-382.

Chen, S., & Wang, C. (2019). Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters. Discrete Dynamics in Nature and Society, 2019(1), 1-28.

Cheraghi, S., & Hosseini-Motlagh, S. M. (2018). Responsive and reliable injured-oriented blood supply chain for disaster relief: a real case study. Annals of Operations Research, 1-39.

Cheraghi, S., Hosseini-Motlagh, S. M., & Ghatreh Samani, M. (2017). Integrated planning for blood platelet production: a robust optimization approach. Journal of Industrial and Systems Engineering, 10(special issue on healthcare), 55-80.

Chopra, S., & Sodhi, M. S. (2004). Supply-chain breakdown. MIT Sloan management review, 46(1), 53-61.

Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1-13.

Derikvand, H., Hajimolana, S. M., & Jabarzadeh, A. (2019). A fuzzy stochastic bi-objective model for blood provision in disastrous time. Journal of Industrial and Systems Engineering, 12(2), 223-245.

Donadoni, M., Roden, S., Scholten, K., Stevenson, M., Caniato, F., van Donk, D. P., & Wieland, A. (2019). The Future of Resilient Supply Chains. In Revisiting Supply Chain Risk (pp. 169-186). Springer, Cham.

Ebrahimi, M., Safari, H., Sadeghi Moghaddam, M. (2017). A Mathematical Model for Power Generation Expansion Planning with Considering Distributed Generation Units and Decreasing Carbon Dioxide. Industrial Management Journal, 9(4), 563-586.

Ellis, S. C., Henry, R. M., & Shockley, J. (2010). Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment. Journal of operations management, 28(1), 34-46.

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. (2016). Marrying supply chain sustainability and resilience: A match made in heaven. Transportation Research Part E: Logistics and Transportation Review, 91, 306-324.

Fazli-Khalaf, M., Fathollahzadeh, K., Mollaei, A., Naderi, B., & Mohammadi, M. (2019). A robust possibilistic programming model for water allocation problem. RAIRO-Operations Research, 53(1), 323-338.

Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2017). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of Operations Research, 1-31.

Ganguly, A., Chatterjee, D., & Rao, H. (2018). The role of resiliency in managing supply chains disruptions. In Supply Chain Risk Management (pp. 237-251). Springer, Singapore.

Gholami-Zanjani, S. M., Jabalameli, M. S., Klibi, W., & Pishvaee, M. S. (2021). A robust location-inventory model for food supply chains operating under disruptions with ripple effects. International Journal of Production Research, 59(1), 301-324.

Gorissen, B. L. (2015). Robust fractional programming. Journal of Optimization Theory and Applications, 166(2), 508-528.

Haeri, A., Hosseini‐Motlagh, S. M., Ghatreh Samani, M. R., & Rezaei, M. (2020). A mixed resilient‐efficient approach toward blood supply chain network design. International Transactions in Operational Research, 27(4), 1962-2001.

Hasanzadeh, M., Maleki, M., Jahangirnia, H., Gholami Jamkarani, R. (2020). Identifying and Prioritizing the Factors Affecting the Resilience of the Iranian Capital Market. Industrial Management Journal, 12(1), 172-205. (in Persian)

Hosseini-Motlagh, S. M., Cheraghi, S., & Ghatreh Samani, M. (2016). A robust optimization model for blood supply chain network design. International Journal of Industrial Engineering & Production Research, 27(4), 425-444.

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.

Huang, Y., Pardalos, P. M., & Zheng, Q. P. (2017). Electrical power unit commitment: deterministic and two-stage stochastic programming models and algorithms. Springer.

Inagaki, M., & Kuroda, K. (2007). Supply chain management in Japan. Supply and Demand Chain Executive, 8(3), 68.

Izadyar, M., Toloie-Eshlaghy, A., Seyed Hosseini, S. (2020). A Model of Sustainability Performance Assessment of LARG Supply Chain Management Practices in Automotive Supply Chain Using System Dynamics. Industrial Management Journal, 12(1), 111-142. (in Persian)

Jabbarzadeh, A., Fahimnia, B., & Seuring, S. (2014). Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application. Transportation Research Part E: Logistics and Transportation Review, 70, 225-244.

Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 116, 178-191.

Jabbarzadeh, A., Oghyani, M., Sadjadi, J. (2015). Provide a robust optimization model for designing the blood supply chain network in crisis situations with regard to reliability. Quality Engineering and Management Journal, 5 (2), 85-96. (in Persian)

Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133.

Kamyabniya, A., Lotfi, M. M., Cai, H., Hosseininasab, H., Yaghoubi, S., & Yih, Y. (2019). A two-phase coordinated logistics planning approach to platelets provision in humanitarian relief operations. IISE Transactions, 51(1), 1-21.

Khalilpourazari, S., & Khamseh, A. A. (2017). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Annals of Operations Research, 1-39.

Ma, H. L., & Wong, W. H. C. (2018). A fuzzy-based House of Risk assessment method for manufacturers in global supply chains. Industrial Management & Data Systems, 118(7), 1463-1476.

Melnyk,S.A.,Closs,D.J.,Griffis,S.E.,Zobel,C.W.,Macdonald,J.R.,2014.Undestanding supplychainresilience.SupplyChainManag.Rev.18(1), 34–41.

Morris, P. (2019). Responding to disruptions in the pharmaceutical supply chain. Clinical Pharmacist, 11(2).

Nahmias, S. (1982). Perishable inventory theory: A review. Operations research, 30(4), 680-708.

Neise, F. (2008). Risk management in stochastic integer programming. Vieweg+ Teubner.

Oke, A., & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail supply chain. International journal of production economics, 118(1), 168-174.

Or, I., & Pierskalla, W. P. (1979). A transportation location-allocation model for regional blood banking. AIIE transactions, 11(2), 86-95.

Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2015). A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making. International Journal of Production Research, 53(24), 7191-7212.

Paydar, M. M., Habibi, M. (2016). Designing a multi-objective multi-level model of blood supply chain in crisis. Supply Chain Management, 17(50), 82-95. (in Persian)

Pourmehdi, M., Paydar, M. M., & Asadi-Gangraj, E. (2020). Scenario-based design of a steel sustainable closed-loop supply chain network considering production technology. Journal of Cleaner Production, 277, 123298.

Rahmani, D. (2019). Designing a robust and dynamic network for the emergency blood supply chain with the risk of disruptions. Annals of Operations Research, 283(1), 613-641.

Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283(1), 1413-1462.

Samani, M. R. G., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450-471.

Sangari, M. S., & Dashtpeyma, M. (2019). An integrated framework of supply chain resilience enablers: a hybrid ISM-FANP approach. International Journal of Business Excellence, 18(2), 242-268.‏

Seyyedi, S., Khatami, M., Amiri, M., Taghavi Fard, M. (2019). Positioning and Optimized Allocation of Transfer Points, Hospitals and Emergency Services Centers to Organize a Crisis Relief Chain, Assuming Screening of Injuries. Industrial Management Journal, 11(1), 1-20. (in Persian)

Sha, Y., & Huang, J. (2012). The multi-period location-allocation problem of engineering emergency blood supply systems. Systems Engineering Procedia, 5, 21-28.

Sheffi, Y., & Rice Jr, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan management review, 47(1), 41.

Shekarian, M. (2018). A Literature Review on the Impact of Antecedents of Supply Chain Resilience on Mitigating Supply Chain Disruptions. In Conference: Annual Meeting of the Decision Sciences Institute Proceedings At: Chicago, IL, USA. International Journal of Production Research, 1, 56-65.

Simchi-Levi, D., Schmidt, W., Wei, Y., Zhang, P. Y., Combs, K., Ge, Y., ... & Zhang, D. (2015). Identifying risks and mitigating disruptions in the automotive supply chain. Interfaces, 45(5), 375-390.

Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J., & Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review. IIE Transactions, 48(2), 89-109.

Sodhi, M. S., & Tang, C. S. (2012). Supply Chain Management. In Managing supply chain risk (pp. 3-12). Springer, Boston, MA.

Thomas, B., Anania, K., DeCicco, A., & Hamm, J. A. (2019). Toward Resiliency in the Joint Blood Supply Chain. Rand health quarterly, 8(3).

Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy sets and systems, 159(2), 193-214.

Verma, M., Dahiya, K., Malik, D., Sehgal, P. K., Devi, R., Soni, A., & Ghalaut, V. G. (2015). Effect of blood storage on complete biochemistry. J Blood Disord Transfus, 6(6), 1-4.

Wagner, S. M., & Bode, C. (2006). An empirical investigation into supply chain vulnerability. Journal of purchasing and supply management, 12(6), 301-312.

Wang, C., & Chen, S. (2020). A distributionally robust optimization for blood supply network considering disasters. Transportation Research Part E: Logistics and Transportation Review, 134, 101840.

Waters, D. (2007). Supply chain risk management: vulnerability and resilience in logistics. Kogan Page Publishers.

Xiao, R., Yu, T., & Gong, X. (2012). Modeling and simulation of ant colony's labor division with constraints for task allocation of resilient supply chains. International Journal on Artificial Intelligence Tools, 21(03), 1240014.

Yaghoubi, S., Hosseini-Motlagh, S. M., Cheraghi, S., & Larimi, N. G. (2019). Designing a robust demand-differentiated platelet supply chain network under disruption and uncertainty. Journal of Ambient Intelligence and Humanized Computing, 1-28.

Zendehdel., M, Bozorgi-Amiri., A., & Omrani., H. (2014). A location Model for Blood Donation Camps with Consideration of Disruption. Journal of Industrial Engineering, 48(1), 33-43. (in Persian)