References
Acar, M., & Kaya, O. (2022). Inventory decisions for humanitarian aid materials considering budget constraints. European Journal of Operational Research, 300(1), 95-111.
Anjomshoae, A., Banomyong, R., Hossein Azadnia, A., Kunz, N., & Blome, C. (2023). Sustainable humanitarian supply chains: A systematic literature review and research propositions. Production Planning & Control, 1-21.
Apte, A. (2010). Humanitarian logistics: A new field of research and action. Foundations and Trends® in Technology, Information and Operations Management, 3(1), 1-100.
Baier, C., & Katoen, J.-P. (2008). Principles of model checking. MIT Press.
Balcik, B., & Beamon, B. M. (2008). Facility location in humanitarian relief. International Journal of Logistics, 11(2), 101-121.
Bayram, V., Tansel, B. Ç., & Yaman, H. (2015). Compromising system and user interests in shelter location and evacuation planning. Transportation Research Part B: Methodological, 72, 146-163.
Birkmann, J. (2007). Risk and vulnerability indicators at different scales: Applicability, usefulness and policy implications. Environmental Hazards, 7(1), 20-31.
Bozorgi Amiri, A., Kamali, A., & Shakibaei, H. (2020). risk assessment by integration approach of FMEA and multi criteria decision-making in the interval valued fuzzy environment: case study hydraulic pump manufacturing industry. Journal of Occupational Hygiene Engineering, 7(1), 1-10.
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.
Chowdhury, S., Emelogu, A., Marufuzzaman, M., Nurre, S. G., & Bian, L. (2017). Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188, 167-184.
Cruz, A. M., Steinberg, L. J., Vetere Arellano, A., Nordvik, J.-P., & Pisano, F. (2004). State of the art in Natech risk management. ISPRA: European Commission Joint Research Centre.
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2012). Social vulnerability to environmental hazards. In: Hazards vulnerability and environmental justice (pp. 143-160). Routledge.
De Vries, H., & Van Wassenhove, L. N. (2020). Do optimization models for humanitarian operations need a paradigm shift? Production and Operations Management, 29(1), 55-61.
Dekker, R., Bloemhof, J., & Mallidis, I. (2012). Operations Research for green logistics–An overview of aspects, issues, contributions and challenges. European Journal of Operational Research, 219(3), 671-679.
Delgoshaei, A., Ariffin, M. K. A. B. M., & Leman, Z. B. (2022). An Effective 4–Phased Framework for Scheduling Job-Shop Manufacturing Systems Using Weighted NSGA-II. Mathematics, 10(23), 4607.
Dubey, R., Bryde, D. J., Foropon, C., Graham, G., Giannakis, M., & Mishra, D. B. (2022). Agility in humanitarian supply chain: An organizational information processing perspective and relational view. Annals of Operations Research, 319, 559–579.
Erbeyoğlu, G., & Bilge, Ü. (2020). A robust disaster preparedness model for effective and fair disaster response. European Journal of Operational Research, 280(2), 479-494.
Faghih-Roohi, S., Ong, Y.-S., Asian, S., & Zhang, A. N. (2016). Dynamic conditional value-at-risk model for routing and scheduling of hazardous material transportation networks. Annals of Operations Research, 247, 715-734.
Fathalikhani, S., Hafezalkotob, A., & Soltani, R. (2018). Cooperation and coopetition among humanitarian organizations: A game theory approach. Kybernetes, 47(8), 1642-1663.
Fonseca i Casas, P. (2023). A continuous process for validation, verification, and accreditation of simulation models. Mathematics, 11(4), 845.
Gharib, Z., Tavakkoli-Moghaddam, R., Bozorgi-Amiri, A., & Yazdani, M. (2022). Post-disaster temporary shelters distribution after a large-scale disaster: an integrated model. Buildings 2022, 12, 414.
Ghasemi, P., Goodarzian, F., & Abraham, A. (2022). A new humanitarian relief logistic network for multi-objective optimization under stochastic programming. Applied Intelligence, 52(1), 1171-1192.
Gholizadeh, Z., Soltani, H., Javid, M., & Azar, M. S. (2020). A New robust approach for reactor network synthesis by combination of mathematical method and NSGAII. International Journal of Chemical Reactor Engineering, 18(1), 20190090.
Goli, A., Shahsavani, I., Fazli, F., Golmohammadi, A.-m., & Tavakkoli-Moghaddam, R. (2023). A comprehensive approach to evaluating the effective factors in implementing a circular supply chain by a hybrid MCDM method. International Journal of Supply and Operations Management, 10(4), 545-563.
Hartley, D., & Starr, S. (2010). Verification and validation. In: Estimating Impact: A Handbook of Computational Methods and Models for Anticipating Economic, Social, Political and Security Effects in International Interventions (pp. 311-336). Springer.
Inan, D. I., Beydoun, G., & Othman, S. H. (2023). Risk assessment and sustainable disaster management. Sustainability, 2023, 15(6), 5254;
Khamseh, A. A., & Saatchi, H. M. (2021). Solving a new bi-objective model for relief logistics in a humanitarian supply chain using bi-objective meta-heuristic algorithms. Scientia Iranica, 28(6), 2415-2427.
Laguna-Salvadó, L., Lauras, M., Okongwu, U., & Comes, T. (2019). A multicriteria Master Planning DSS for a sustainable humanitarian supply chain. Annals of Operations Research, 283, 1303-1343.
Li, A. C., Nozick, L., Xu, N., & Davidson, R. (2012). Shelter location and transportation planning under hurricane conditions. Transportation Research Part E: Logistics and Transportation Review, 48(4), 715-729.
Liang, H., Zhang, X., Fang, F., & Chen, X. (2021). An optimization method for determining the emergency action by considering compatibilities and collaborative relationship. Kybernetes, 50(2), 443-466.
Lindell, M. K., & Perry, R. W. (2012). The protective action decision model: Theoretical modifications and additional evidence. Risk Analysis: An International Journal, 32(4), 616-632.
Liu, J., & Xie, K. (2016). Emergency supplies requisition negotiation principle of government in disasters. Kybernetes, 45(8), 1174-1193.
Long, J., Zheng, Z., Gao, X., & Pardalos, P. M. (2016). A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants. Journal of the Operational Research Society, 67(9), 1184-1199.
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.
Mengyue, Y., & Keping, L. (2020). Multi-objective vehicle routing problem based on NSGA-II. 2020 IEEE 3rd International Conference on Information Systems and Computer Aided Education (ICISCAE), Dalian, China, 27-29 September 2020.
Modgil, S., Singh, R. K., & Foropon, C. (2022). Quality management in humanitarian operations and disaster relief management: A review and future research directions. Annals of Operations Research, 319, 1045–1098.
Najafi, M., Eshghi, K., & Dullaert, W. (2013). A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review, 49(1), 217-249.
Ojha, M., Singh, K. P., Chakraborty, P., & Verma, S. (2019). A review of multi-objective optimisation and decision making using evolutionary algorithms. International Journal of Bio-Inspired Computation, 14(2), 69-84.
Paciarotti, C., Piotrowicz, W. D., & Fenton, G. (2021). Humanitarian logistics and supply chain standards. Literature review and view from practice. Journal of Humanitarian Logistics and Supply Chain Management, 11(3), 550-573.
Pasandideh, S. H. R., Niaki, S. T. A., & Asadi, K. (2015). Bi-objective optimization of a multi-product multi-period three-echelon supply chain problem under uncertain environments: NSGA-II and NRGA. Information Sciences, 292, 57-74.
Paul, J. A., & MacDonald, L. (2016). Location and capacity allocations decisions to mitigate the impacts of unexpected disasters. European Journal of Operational Research, 251(1), 252-263.
Paul, S. K., Asian, S., Goh, M., & Torabi, S. A. (2019). Managing sudden transportation disruptions in supply chains under delivery delay and quantity loss. Annals of Operations Research, 273, 783-814.
Pyakurel, U., Nath, H. N., & Dhamala, T. N. (2019). Partial contraflow with path reversals for evacuation planning. Annals of Operations Research, 283, 591-612.
Rabiei, P., Arias-Aranda, D., & Stantchev, V. (2023). Introducing a novel multi-objective optimization model for Volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II. Expert Systems with Applications, 204, 117339.
Ramezanian, R., & Behboodi, Z. (2017). Blood supply chain network design under uncertainties in supply and demand considering social aspects. Transportation Research Part E: Logistics and Transportation Review, 104, 69-82.
Ransikarbum, K., & Mason, S. J. (2022). A bi-objective optimisation of post-disaster relief distribution and short-term network restoration using hybrid NSGA-II algorithm. International Journal of Production Research, 60(24), 7485-7501.
Rath, S., & Gutjahr, W. J. (2014). A math-heuristic for the warehouse location–routing problem in disaster relief. Computers & Operations Research, 42, 25-39.
Rezaei Somarin, A., Asian, S., Jolai, F., & Chen, S. (2018). Flexibility in service parts supply chain: a study on emergency resupply in aviation MRO. International Journal of Production Research, 56(10), 3547-3562.
Roblek, V., Dimovski, V., Mesko, M., & Peterlin, J. (2022). Evolution of organisational agility: A bibliometric study. Kybernetes, 51(13), 119-137.
Roy, C. J., & Oberkampf, W. L. (2011). A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing. Computer Methods in Applied Mechanics and Engineering, 200(25-28), 2131-2144.
Sabouhi, F., Bozorgi-Amiri, A., Moshref-Javadi, M., & Heydari, M. (2019). An integrated routing and scheduling model for evacuation and commodity distribution in large-scale disaster relief operations: A case study. Annals of Operations Research, 283, 643-677.
Sabouhi, F., Bozorgi-Amiri, A., & Vaez, P. (2021). Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes, 50(9), 2632-2650.
Saeidian, B., Mesgari, M. S., & Ghodousi, M. (2016). Evaluation and comparison of genetic algorithm and bees algorithm for location–allocation of earthquake relief centers. International Journal of Disaster Risk Reduction, 15, 94-107.
Sargent, R. G. (2015). Model verification and validation. In: Modeling and simulation in the systems engineering life cycle: Core concepts and accompanying lectures (pp. 57-65). Springer.
Seraji, H., Tavakkoli-Moghaddam, R., Asian, S., & Kaur, H. (2022). An integrative location-allocation model for humanitarian logistics with distributive injustice and dissatisfaction under uncertainty. Annals of Operations Research, 319(1), 211-257.
Shakibaei, H., Farhadi-Ramin, M. R., Alipour-Vaezi, M., Aghsami, A., & Rabbani, M. (2024). Designing a post-disaster humanitarian supply chain using machine learning and multi-criteria decision-making techniques. Kybernetes, 53(5), 1682-1709.
Shakibaei, H., Moosavi, S. A., Aghsami, A., & Rabbani, M. (2024). Designing a sustainable-resilient humanitarian supply chain for post-disaster relief process, an earthquake case study in Haiti. Journal of Humanitarian Logistics and Supply Chain Management, Article in Press
Shakibaei, H., Seifi, S., & Zhuang, J. (2024). A data‐driven and cost‐oriented FMEA–MCDM approach to risk assessment and ranking in a fuzzy environment: A hydraulic pump factory case study. Risk Analysis.
Sharma, B., Ramkumar, M., Subramanian, N., & Malhotra, B. (2019). Dynamic temporary blood facility location-allocation during and post-disaster periods. Annals of Operations Research, 283, 705-736.
Talebi, E., Shaabani, M., & Rabbani, M. (2022). Bi-Objective Model for Ambulance Routing for Disaster Response by Considering Priority of Patients. International Journal of Supply and Operations Management, 9(1), 80-94.
Tofighi, S., Torabi, S. A., & Mansouri, S. A. (2016). Humanitarian logistics network design under mixed uncertainty. European Journal of Operational Research, 250(1), 239-250.
Wang, F., Pei, Z., & Dong, L. (2021). Emergency resource allocation for multi-period post-disaster using multi-objective cellular genetic algorithm. IEEE Access, 9, 108907-108917.
Wang, X., Wu, Y., Liang, L., & Huang, Z. (2016). Service outsourcing and disaster response methods in a relief supply chain. Annals of Operations Research, 240, 471-487.
Xu, W., Xu, J., Proverbs, D., & Zhang, Y. (2024). A hybrid decision-making approach for locating rescue materials storage points under public emergencies. Kybernetes, 53(1), 293-313.
Yi, W., & Kumar, A. (2007). Ant colony optimization for disaster relief operations. Transportation Research Part E: Logistics and Transportation Review, 43(6), 660-672.
Yuan, M., Li, Y., Zhang, L., & Pei, F. (2021). Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm. Robotics and Computer-Integrated Manufacturing, 71, 102141.
Zhu, L., Gong, Y., Xu, Y., & Gu, J. (2019). Emergency relief routing models for injured victims considering equity and priority. Annals of Operations Research, 283, 1573-1606.
Zokaee, S., Bozorgi-Amiri, A., & Sadjadi, S. J. (2016). A robust optimization model for humanitarian relief chain design under uncertainty. Applied Mathematical Modelling, 40(17-18), 7996-8016.
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