Humanitarian Smart Supply Chain: Classification and New Trends for Future Research

محورهای موضوعی : Design of Experiment

Fatemeh Kheildar ¹ (Department of industrial engineering, faculty of engineering, Bu-Ali Sina University, Hamedan, Iran)
Parvaneh Samouei ² (Department of industrial engineering, faculty of engineering, Bu-Ali Sina University, Hamedan, Iran)
Jalal Ashayeri ³ (TIAS School for Business and Society, Tilburg University, Warandelaan 2, 5037 AB Tilburg, Netherlands)

تاریخ دریافت : 1402/01/18 تاریخ پذیرش : 1402/04/13 تاریخ انتشار : 1402/06/10

کلید واژه: Queue theory, Machine Learning, Location, Humanitarian Supply Chain, Patient Classification, Relief Team and Patient Classification,

چکیده مقاله :

During the crisis, relief supply chain management (also known as humanitarian supply chain management) has received great attention these days. The core questions facing many humanitarian organizations are: where are their strengths/weaknesses? Are they positioned to be effective in their supply chain system? What challenges do you need to overcome? What do they need to do to take advantage of the technological opportunities offered nowadays? These questions have been addressed them extensively during the past two decades. This paper tries to review and classify some of the papers carried out in key areas of the humanitarian supply chain such as location, certainty and uncertainty, relief teams and injured (patient) classification, machine learning, queue theory, the employed research methods, solution methods, and the type of objective functions. The paper begins first to define what the “humanitarian” ecosystem may include, and which actors play important roles. After, certain critical views of the humanitarian relief supply chain are examined. The critical views of the humanitarian relief supply chain would help researchers to introduce further research orientations and areas to overcome crises in the real world.

چکیده انگلیسی:

During the crisis, relief supply chain management (also known as humanitarian supply chain management) has received great attention these days. The core questions facing many humanitarian organizations are: where are their strengths/weaknesses? Are they positioned to be effective in their supply chain system? What challenges do you need to overcome? What do they need to do to take advantage of the technological opportunities offered nowadays? These questions have been addressed them extensively during the past two decades. This paper tries to review and classify some of the papers carried out in key areas of the humanitarian supply chain such as location, certainty and uncertainty, relief teams and injured (patient) classification, machine learning, queue theory, the employed research methods, solution methods, and the type of objective functions. The paper begins first to define what the “humanitarian” ecosystem may include, and which actors play important roles. After, certain critical views of the humanitarian relief supply chain are examined. The critical views of the humanitarian relief supply chain would help researchers to introduce further research orientations and areas to overcome crises in the real world.

منابع و مأخذ:

Abidi, H., Kandel, C., Klumpp, M., Zinnert, S. )2015(. Sustainable humanitarian logistics optimization—A hub concept for Germany based on the Shapley value. Lecture Notes in Logistics, 129-148.

Alaswad, S., Salman, S. )2020(. Humanitarian aid and relief distribution (HARD) game. Advances in Engineering Education, 8(2), 1-30.

Alem, D., Clark, A. )2015(. Insights from two-stage stochastic programming in emergency logistics. ISCRAM 2015 Conference Proceedings - 12th International Conference on Information Systems for Crisis Response and Management 2015-January.

An, S. Cui, N. Bai, T. Xie, W. Chen, M. Ouyang, Y. )2015(. Reliable emergency service facility location under facility disruption, en-route congestion and in-facility queuing. Transportation Research Part E: Logistics and Transportation Review, 82, 199-216.

Anjomshoae, A., Hassan, A., Samuel, C., Yew, W.K. )2016(. Effect of information sharing and capacity adjustment on healthcare service supply Chain: The case of flood disaster. Proceedings of the International Conference on Industrial Engineering and Operations Management 8-10 March 2016, 3390-3402.

Azimi, S. Delavar, M. R. Rajabifard, A. (2019). Developing a Multi-agent Based Modeling for Smart Search and Rescue Operation. Intelligent Systems for Crisis Management, 133–157.

Baharmand, H. Comes, T. Lauras, M. (2017). Managing in-country transportation risks in humanitarian supply chains by logistics service providers: Insights from the 2015 Nepal earthquake. International Journal of Disaster Risk Reduction, 24, 549-559.

Baraka, J.-C.M., Yadavalli, V.S.S., Singh, R. (2017). A transportation model for an effective disaster relief operation in the SADC region. South African Journal of Industrial Engineering, 28(2), 46-58.

Barzinpour, F., Esmaeili, V. (2014). A multi-objective relief chain location distribution model for urban disaster management. International Journal of Advanced Manufacturing Technology, 70(5-8), 1291-1302.

Behl, A., Dutta, P., Gupta, S. (2019). Critical success factors for humanitarian supply chain management: A grey DEMATEL approach. IFAC-PapersOnLine, 52(13), 159-164.

Beiki, H., Seyedhosseini, S.M., Ghezavati, V.R., Seyedaliakbar, S.M. (2020). A location-routing model for assessment of the injured people and relief distribution under uncertainty. International Journal of Engineering, Transactions A: Basics, 33(7),1274-1284.

Boostani A., Jolai F., Bozorgi-Amiri A. (2021). Designing a sustainable humanitarian relief logistics model in pre- and post-disaster management. International Journal of Sustainable Transportation, 15(8), 604 – 620.

Bravo, R., Leiras, A. Oliveira, F. (2019). The Use of UAVs in Humanitarian Relief: An Application of POMDP-Based Methodology for Finding Victims. Production and Operations Management, 28(2), 421-440.

Budak, A. Kaya, I. Karaşan, A. Erdoğan, M. (2020). Real-time location systems selection by using a fuzzy MCDM approach: An application in humanitarian relief logistics. Applied Soft Computing, 92, 106322.

Bune, G. Leon, F. Gabriela, M. Atanasiu, M. (2016). Post disaster Evacuation Scenarios Using Multiagent System. Journal of Computing in Civil Engineering, 30(6), 05016002.

Cao, C. Liu, Y. Tang, O. Gao, X. (2021). A fuzzy bi-level optimization model for multi-period post-disaster relief distribution in sustainable humanitarian supply chains. International Journal of Production Economics, 235, 108081.

Cao, C., Li, C., Yang, Q., Liu, Y., Qu, T. (2018). A novel multi-objective programming model of relief distribution for sustainable disaster supply chain in large-scale natural disasters. Journal of Cleaner Production, 147, 1422-1435.

Chakravarty, A.K. (2014). Humanitarian relief chain: Rapid response under uncertainty. International Journal of Production Economics, 151, 146-157.

Charles, A., Lauras, M., Van Wassenhove, L.N., Dupont, L. (2016). Designing an efficient humanitarian supply network. Journal of Operations Management, 47-48, 58-70.

Cheng, J. Feng, X. Bai, X. (2021). Modeling equitable and effective distribution problem in humanitarian relief logistics by robust goal programming. Computers & Industrial Engineering, 155, 107183.

Connelly, E.B., Lambert, J.H., Thekdi, S.A. (2016). Robust Investments in Humanitarian Logistics and Supply Chains for Disaster Resilience and Sustainable Communities. Natural Hazards Review, 17(1), 04015017.

Cui, S., Liu, S., Tang, X., Zhu, T. (2019). Emergency material allocation problem considering post-disaster impact. proceedings of 2019 8th International Conference on Industrial Technology and Management, ICITM 2019, 8710729, 290-294.

Davis, LB. Samanlioglu, F. Qu, X. Root, S. (2013). Inventory planning and coordination in disaster relief efforts. International Journal of Production Economics, 141(2), 561-573.

Deng, F., Zhang, X., Liang, X., Guo, Z., Bao, C. (2016). Earthquake disaster emergency supply chain performance evaluation based on triangular fuzzy numbers. IEEE International Conference on Industrial Engineering and Engineering Management, 7798124, 1483-1487.

Diaz. R., Behr. J.G., Longo. F., Padovano. A. (2020). Supply Chain Modeling in the Aftermath of a Disaster: A System Dynamics Approach in Housing Recovery. IEEE Transactions on Engineering Management, 67(3),8935181, 531-544.

Doodman, M., Shokr, I., Bozorgi-Amiri, A., Jolai, F. (2019). Pre-positioning and dynamic operations planning in pre- and post-disaster phases with lateral transshipment under uncertainty and disruption. Journal of Industrial Engineering International, 15, 53-68.

Edrissi, A. Poorzahedy, H. Nassiri, H. Nourinejad, M. (2013). A multi-agent optimization formulation of earthquake disaster prevention and management. European Journal of Operational Research, 229, 261–275.

Ergun, Ö. Heier Stamm, JL. Keskinocak, P. Swann, JL. (2010). Waffle House Restaurants hurricane response: A case study. International Journal of Production Economics, 126(1), 111-120.

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.

Fereiduni, M., Shahanaghi, K. (2016). A robust optimization model for blood supply chain in emergency situations. International Journal of Industrial Engineering Computations, 7(4), 535-554.

Fontainha, T.C., Silva, L.D.O., de Lira, W.M., (...), Bandeira, R.A.D.M., Scavarda, L.F. (2020). Reference process model for disaster response operations. International Journal of Logistics Research and Applications, 1-26.

Francis, R. L, White, J. A., Leon F. McGinnis. (1974). Facility layout and location: An analytical approach. Englewood Cliffs, NJ, US: Prentice-Hall.

Gamage, V.Y., Olapiriyakul, S. (2020). Flood-resilient Supply Chain Network Design: A Case Study of Eastern Thailand. 7th International Conference on Industrial Engineering and Applications, ICIEA 9102103, 459-463.

Ganguly, K.K., Padhy, R.K., Rai, S.S. (2017). Managing the humanitarian supply chain: a fuzzy logic approach. International Journal of Disaster Resilience in the Built Environment, 8(5), 521-536.

Gao, X. Cao, C. (2020). Multi-commodity rebalancing and transportation planning considering traffic congestion and uncertainties in disaster response. Computers & Industrial Engineering, 149, 106782.

Gatignon, A., Van Wassenhove, L.N., Charles, A. (2010). The Yogyakarta earthquake: Humanitarian relief through IFRC's decentralized supply chain. International Journal of Production Economics, 126(1), 102-110.

Ghaffari, Z., Nasiri, M.M., Bozorgi-Amiri, A., Rahbari, A. (2020). Emergency supply chain scheduling problem with multiple resources in disaster relief operations. Transport Science, 16(3), 930-956.

Ghasemi, S. (2019). The Location Allocation Problem of after Disaster Blood Supply Chain. Proceedings of 2019 15th Iran International Industrial Engineering Conference, IIIEC 2019, 8720635, 127-131.

Ghezavati, V., Soltanzadeh, F., Hafezalkotob, A. (2015). Optimization of reliability for a hierarchical facility location problem under disaster relief situations by a chance-constrained programming and robust optimization. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229(6), 542-555.

Gil, J.C.S., McNeil, S. (2015). Supply Chain Outsourcing in Response to Manmade and Natural Disasters in Colombia, a Humanitarian Logistics Perspective. Procedia Engineering, 107, 110-121.

Gobaco, B., Lahoz, M., Ng, M., Sy, D. (2016). Prepositioning of relief goods under network uncertainty for tropical cyclones with information updates: A Humanitarian supply chain. Proceedings of the International Conference on Industrial Engineering and Operations Management, 8-10 March 2016, 217-1228.

Gökçe, MA. Ercan, E. (2019). Multi-Period Vehicle Routing & Replenishment Problem of Neighborhood Disaster Stations for Pre-Disaster Humanitarian Relief Logistics. IFAC-PapersOnLine, 52(13), 2614-2619.

Guan, X. Zhou, H. Li, M. Zhou, L. Chen, H. (2021). Multilevel coverage location model of earthquake relief material storage repository considering distribution time sequence characteristics. Journal of Traffic and Transportation Engineering (English Edition), 8(2), 209-224.

Gulzari, A. Tarakci, H. (2021). A healthcare location-allocation model with an application of telemedicine for an earthquake response phase. International Journal of Disaster Risk Reduction, 55, 102100.

Guo, H., Peng, C. (2019). Research on Two-stage Multi-objective Integer Programming Model of Helicopter Distribution Materials in Emergency Logistics. IOP Conference Series: Materials Science and Engineering, 677(4), 042031.

Gutierrez, M.T.E., Mutuc, J.E.S. (2018). A Model for Humanitarian Supply Chain: An Operation Research Approach. Procedia Engineering, 212, 659-666.

Habibi-Kouchaksaraei, M. Paydar, MM. Asadi-Gangraj, M. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling, 55, 583-599.

Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. A real-world application, Engineering Applications of Artificial Intelligence, 90, 103493.

Hamdan, B., Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.

Han, Y., Guan, X., Shi, L. (2010). Optimal supply location selection and routing for emergency material delivery with uncertain demands. ICINA 2010 - 2010 International Conference on Information, Networking and Automation, Proceedings 1, 5636428, V187-V192.

Harke, J., de Leeuw, S. (2015). Enhancing Sustainability in Managing Inventory Prepositioning Networks for Disaster Relief Through a Simulation Game. Lecture Notes in Logistics, 215-233.

Hashemipour, M. Stuban, S. Dever, J. (2018). A disaster multiagent coordination simulation system to evaluate the design of a first-response team. Systems Engineering, 21(4), 322-344.

Hong, J.-D., Jeong, K.-Y. (2019). Humanitarian supply chain network design using data envelopment analysis and multi-objective programming models. European Journal of Industrial Engineering, 13(5), 651-680.

Horner, M.W., Downs, J.A. (2007). Testing a flexible geographic information system-based network flow model for routing hurricane disaster relief goods. Transportation Research Record, 2022, 47-54.

Hu, Z.-H. (2011). A container multimodal transportation scheduling approach based on immune affinity model for emergency relief. Expert Systems with Applications, 38(3), 2632-2639.

Hu, L.H. FengHan, C. PengMeng, L. (2017). Stochastic optimization for joint decision making of inventory and procurement in humanitarian relief. Computers & Industrial Engineering, 111, 39-49.

Huwang, C. l, Lin, M. l. (1987). Group decision making under multiple criteria, Springer-Verlag berlin Heidelberg.

Ismail, I. (2021). A possibilistic mathematical programming model to control the flow of relief commodities in humanitarian supply chains. Computers & Industrial Engineering, 157, 107305.

Islam, S. Parr, S. Prazenica, R. Liu, D. Namilae, S. (2021). Predictive modelling of fuel shortages during hurricane evacuation: An epidemiological approach. IET Intelligent Transport Systems, 15(8), 1064-1075.

Iizuka, Y. Iizuka, K. (2015). Disaster Evacuation Assistance System based on Multi-agent Cooperation, 48th Hawaii International Conference on System Sciences.

Javadian, N., Modarres, S., Bozorgi, A. (2017). A Bi-objective stochastic optimization model for humanitarian relief chain by using evolutionary algorithms. International Journal of Engineering, Transactions A: Basics, 30(10), 1526-1537.

Ji, G. Zhu, C. (2012). A Study on Emergency Supply Chain and Risk Based on Urgent Relief Service in Disasters. Systems Engineering Procedia, 5, 313-325.

Kaddoussi, A. Zoghlami, N. Hammadi, S. Zgaya, H. (2013). An Agent-Based Distributed Scheduling for Crisis Management Supply Chain. International Journal of Computational Intelligence Systems, 6(1), 156-173.

Kamyabniya, A., Noormohammadzadeh, Z., Sauré, A., Patrick, J. (2021). A robust integrated logistics model for age-based multi-group platelets in disaster relief operations. Transportation Research Part E, 152, 102371.

Kaur, H., Singh, S.P. (2020). Disaster resilient proactive and reactive procurement models for humanitarian supply chain. Production Planning and Control, 19-53.

Kelle, P., Schneider, H., Yi, H. (2014). Decision alternatives between expected cost minimization and worst-case scenario in emergency supply - Second revision. International Journal of Production Economics, 157(1), 250-260.

Kondo, A. (2018). The effects of supply chain disruptions caused by the Great East Japan Earthquake on workers. Japan and the World Economy, 47, 40-50.

Kumar, S., Havey, T. (2013). Before and after disaster strikes: A relief supply chain decision support framework. International Journal of Production Economics, 145(2), 613-629.

Kwasinski, A., Weaver, W.W., Chapman, P.L., Krein, P.T. (2006). Telecommunications power plant damage assessment caused by Hurricane Katrina - Site survey and follow-up results. INTELEC, International Telecommunications Energy Conference (Proceedings) 4018146.

Lauras, M., Vargas, J., Dupont, L., Charles, A. (2014). A location-allocation model for more consistent humanitarian supply chains. Lecture Notes in Business Information Processing, 196, 1-12.

Lee, H. Lee, T. (2021). Multi-agent reinforcement learning algorithm to solve a partially-observable multi-agent problem in disaster response. European Journal of Operational Research, 291(1), 296-308.

Li, X.-Y., Zheng, Y., Wang, Y.-X. (2013). Emergency decision model for supply chain distribution in emergency response. Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, 6567245, 300-306.

Liu, X., Song, X. (2019). Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-Papers Online, 52(13), 778-783.

Liu, Y. Lei, H. Wu, ZH. Zhang, D. (2019). A robust model predictive control approach for post-disaster relief distribution. Computers & Industrial Engineering, 135, 1253-1270.

Liu, Y. Lei, H. Wu, ZH. Zhang, D. (2018). Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modelling, 55, 262-280.

MacKenzie, C.A., Barker, K., Santos, J.R. (2014). Modeling a severe supply chain disruption and post-disaster decision making with application to the Japanese earthquake and tsunami. IIE Transactions (Institute of Industrial Engineers), 46(12), 1243-1260.

MacKenzie, C.A., Santos, J.R., Barker, K. (2012). Measuring changes in international production from a disruption: Case study of the Japanese earthquake and tsunami. International Journal of Production Economics, 138(2), 293-302.

Manopiniwes, W., Irohara, T. (2017). Stochastic optimization model for integrated decisions on relief supply chains: preparedness for disaster response. International Journal of Production Research, 55(4), 979-996.

Manopiniwes, W., Nagasawa, K., Irohara, T. (2015). Facility location alternatives between expected and worst-case time performance in humanitarian relief logistics. Journal of Japan Industrial Management Association, 66(2E), 142-153.

Mansoori, S. Bozorgi-Amiri, A. Pishvaee, MS. (2020). A robust multi-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications, 32, 2183–2203.

Memari, P. Tavakkoli-Moghaddam, R. Partovi, M. Zabihian, A. (2018). Fuzzy Dynamic Location-Allocation Problem with Temporary Multi-Medical Centers in Disaster Management. IFAC-PapersOnLine, 51(11), 1554-1560.

Moeen-Moghadas, F. Monabbati, E. Taghizadeh-Kakhki, H. (2013). Emergency Location Problems with an M/G/k Queueing System. Iranian Journal of Operations Research, 4(1), 1-13.

Mora-Ochomogo, I., Serrato, M., Mora-Vargas, J. (2020). A monotone optimal policy for collection points in disaster relief operations. Proceedings of the 2016 Industrial and Systems Engineering Research Conference, ISERC 2016, 860-865.

Mousa, A.A., Higazy, M., Abdel-Khalek, S., Hussein, M.A., Farouk. (2021). multi-objective reference point based enriched swarm optimization with an application to blood supply chain under natural disaster. Journal of Intelligent & Fuzzy Systems, 41(1), 715-733.

Mu, J., Liang, L.L. (2014). Multi-stage emergency resources management based on supply chain. Applied Mechanics and Materials, 635-637, 1780-1783.

Mushanyuri, B.E., Ngcamu, B.S. (2020). The effectiveness of humanitarian supply chain management in Zimbabwe. Journal of Transport and Supply Chain Management, 14(a505), 1-11.

Naghipour, M., Bashiri, M. (2019). Designing a Bi-Objective Stochastic Blood Supply Chain Network in a Disaster. Proceedings of 2019 15th Iran International Industrial Engineering Conference, IIIEC 20198720727, 171-177.

Ni, C., de Souza, R., Lu, Q., Goh, M. (2015). Emergency Preparedness of Humanitarian Organizations: A System Dynamics Approach. Lecture Notes in Logistics, 113-127.

Nie, H. Chen, Y. Xia, Y. Huang, S. Liu, B. (2020). Optimizing the Post-Disaster Control of Islanded Microgrid: A Multi-Agent Deep Reinforcement Learning Approach. IEEE Access, 8, 153455-153469.

Nozhati, S., Rosenheim, N., Ellingwood, B.R., Mahmoud, H., Perez, M. (2019). Probabilistic framework for evaluating food security of households in the aftermath of a disaster. Structure and Infrastructure Engineering, 15(8), 1060-1074.

Oloruntoba, O. (2010). An analysis of the Cyclone Larry emergency relief chain: Some key success factors. International Journal of Production Economics, 126(1), 85-101.

Ozbay, K., Ozguven, E.E. (2007). Stochastic humanitarian inventory control model for disaster planning. Transportation Research Record, 63-75.

Patil, A. Shardeo, V. Dwivedi, A. Madaan, J. Varma, N. (2021). Barriers to sustainability in humanitarian medical supply chains. Sustainable Production and Consumption, 27, 1794-1807.

Pfeiffer, K.B., Burdi, C., Schlueter, S. (2017). Local supply chains: The disaster management perspective. International Journal of Safety and Security Engineering, 7(3), 399-405.

Pido, M. (2004). Computer Simulation in Management Science, Wiley.

Praneetpholkrang, P., Huynh, V.N., Kanjanawattana, S. (2021). Bi-objective optimization model for determining shelter location-allocation in humanitarian relief logistics. ICORES-Proceedings of the 10th International Conference on Operations Research and Enterprise Systems, 387-393.

Rahimzadeh Dehaghani, A. Nawaz, M. Sultanie, R. (2021). Mathematical modeling for optimizing the blood supply chain network. Modern Supply Chain Research and Applications, 3(3), 174-190.

Ransikarbum, K., Mason, S.J. (2016). Goal programming-based post-disaster decision making for integrated relief distribution and early-stage network restoration. International Journal of Production Economics, 182, 324-341.

Ren, Y., Hajiebrahimi, S., Azad, M., Awasthi, A., Salah, S. (2020). Humanitarian aid for Wuhan with crisis logistics management approach. Proceedings of the International Conference on Industrial Engineering and Operations Management0(March), 2975-2986.

Rezaei M., Afsahi M., Shafiee M., Patriksson M. (2020). A bi-objective optimization framework for designing an efficient fuel supply chain network in post-earthquakes. Computers and Industrial Engineering, 147, 106654

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.

Roa, S. S. (2009). engineering optimization - theory and practice, Wiley, Newyork.

Saatchi, H.M. Khamseh, A.A. Tavakkoli-Moghaddam, R. (2021). Solving a new bi-objective model for relief logistics in a humanitarian supply chain using bi-objective meta-heuristic algorithms. Scientia Iranica, 28(5 E), 2948-2971.

Safaei, A. S. Farsad, S. Paydar, MM. (2018). Robust bi-level optimization of relief logistics operations. Applied Mathematical Modelling, 56, 359-380.

Saghehei, E., Memariani, A., Bozorgi-Amiri, A. (2021). A Bi-level programming approach for pre-positioning emergency warehouses. International Journal of Engineering, Transactions A: Basics 34(1), 128-139.

Sayarshad. H.R. Du. X. H. Gao. O. (2020). Dynamic post-disaster debris clearance problem with repositioning of clearance equipment items under partially observable information. Transportation Research Part B: Methodological, 138, 352-372.

Schumann-Bölsche, D. (2015). Managing Hazardous Goods in Humanitarian Supply Chains. Lecture Notes in Logistics, 151-168.

Sharma, M.G., Srivastava, S.K. (2016). Leveraging the social welfare chain to provide resilience during disaster. International Journal of Logistics Research and Applications, 19(6), 509-519.

Shen, J. Kang, K. Shi, Y. Li, Y. (2012). Lessons learned from the Wenchuan earthquake. Journal of Evidence-Based Medicine 5(2), 75-88.

Song, J.M., Chen, W., Lei, L. (2018). Supply chain flexibility and operations optimization under demand uncertainty: a case in disaster relief. International Journal of Production Research, 56(10), 3699-3713.

Stauffer, J.M., Pedraza-Martinez, A.J., Van Wassenhove, L.N. (2016). Temporary hubs for the global vehicle supply chain in humanitarian operations. Production and Operations Management, 25(2), 192-209.

Sun, J., Chai, R., Nakade, K. (2018). A study of stochastic optimization problem for humanitarian supply chain management. Journal of Advanced Mechanical Design, Systems and Manufacturing, 12(3), 1-11, JAMDSM0066.

Sun, H. Wang, Y. Xue, Y. (2021). A bi-objective robust optimization model for disaster response planning under uncertainties. Computers & Industrial Engineering, 155, 107213.

Sutrisno, A., Handayani, D., Caesarendra, W., Gunawan, I. (2020). Categorization of Reliability Performance Indicators of Humanitarian Response Supply Chain. IOP Conference Series: Materials Science and Engineering, 722(1), 012007.

Syahrir, I., Vanany, I. 2015. Healthcare and Disaster Supply Chain: Literature Review and Future Research. Procedia Manufacturing, 4, 2-9.

Tal, A, B., Chung, B.D., Mandala, S.R., Yao, T. (2011). Robust optimization for emergency logistics planning: Risk mitigation in humanitarian relief supply chains. Transportation Research Part B: Methodological, 45(8), 1177-1189.

Taskin, S., Lodree Jr., E.J. (2010). Inventory decisions for emergency supplies based on hurricane count predictions. International Journal of Production Economics, 126(1), 66-75.

Tian, R., Yin, P., Li, Q. (2011). Research on the optimization of relief supplies distribution routing based on multiple models. Advanced Materials Research, 228-229, 883-888.

Torabi, AS., Shokr, I., Tofighi, S., Heydari, J. (2018). Integrated relief pre-positioning and procurement planning in humanitarian supply chains. Transportation Research Part E: Logistics and Transportation Review, 113, 123-146.

Upadhyay, A., Mukhuty, S., Kumari, S., Garza-Reyes, J.A., Shukla, V. (2020). A review of lean and agile management in humanitarian supply chains: analyzing the pre-disaster and post-disaster phases and future directions. Production Planning and Control, DOI: 10.1080/09537287. 2020.1834133.

Venkatesh, V.G., Dubey, R., Ali, S.S. (2014). Disaster relief operations and continuous aid program in human supply networks: Are they congruent? —an analysis. Advances in Intelligent Systems and Computing, 259, 959-973.

Velasquez G. A., Mayorga, M. E., Özaltın O.Y. (2021). Prepositioning disaster relief supplies using robust optimization. IISE Transactions, 52(10), 1122–11402.

Wang, X., Choi, T.-M., Liu, H., Yue, X. (2018). A novel hybrid ant colony optimization algorithm for emergency transportation problems during post-disaster. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(4), 545-556.

Wei, X., Al-Refaie, A., Robles, M., Noche, B. (2015). A Sustainable Humanitarian Relief Network Study for the Wenchuan Earthquake. Lecture Notes in Logistics, 193-213.

Widera, A., Lechtenberg, S., Gurczik, G., Bähr, S., Hellingrath, B. (2017). Integrated logistics and transport planning in disaster relief operations. Proceedings of the International ISCRAM Conference 2017-May, 752-764.

Xavier, I.R. de Mello Bandeira, R.A. de Oliveira Silva, L. de Paula Fontainhas Bandeira, A. Gouvêa Campos, V.B. (2021). Planning the use of helicopters for supply distribution in response operations to sudden disasters. Gestao e Producao, 28(1), e5000.

Xiang, ZH. Zhuang, J. (2016). A medical resource allocation model for serving emergency victims with deteriorating health conditions. Annals of Operations Research, 236(1), 177-196.

Yu, J. Zhang, C. Wen, J. Li, W. Liu, R. Xu, H. (2018). Integrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters. International Journal of Geographical Information Science, 32(1), 1-27.

Zavvar Sabegh, M.H., Mohammadi, M., Naderi, B. (2017). Multi-objective optimization considering quality concepts in a green healthcare supply chain for natural disaster response: neural network approaches. International Journal of Systems Assurance Engineering and Management, 8, 1689-1703.

Zhang, Y., Diabat, A., Zhang, Z.-H. (2021). Reliable closed-loop supply chain design problem under facility-type-dependent probabilistic disruptions. Transportation Research Part B, 146, 180–209.

Zokaee, M., Tavakkoli-Moghaddam, R., Rahimi, Y. (2021). Post-disaster reconstruction supply chain: Empirical optimization study. Automation in Construction, 129, 103811.

Zokaee, S. Bozorgi-Amiri, A. Sadjadi, S. G. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied Mathematical Modelling, 40(17–18), 7996-8016.