Abolghasemian, M., Kanai, A. G., & Daneshmandmehr, M. (2020). A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies, 13(4), 705-732.
https://doi.org/10.22059/ijms.2020.294809.673898
Aghajani, M., Torabi, S. A., & Heydari, J. (2020). A novel option contract integrated with supplier selection and inventory prepositioning for humanitarian relief supply chains. Socio-Economic Planning Sciences, 71, 100780.
https://doi.org/10.1016/j.seps.2019.100780
Alikhani, S., Ghiyasi, T. (2025). Global Energy Transition Influence in Urban Development: Digital Heritage Preservation Methods Approaches. In: Mehan, A. (eds) After Oil : A Comparative Analysis of Oil Heritage, Urban Transformations, and Resilience Paradigms. Springer, Cham.
https://doi.org/10.1007/978-3-031-92188-9_22
Amiri, M.K., Zaferani, S.P.G., Emami, M.R.S., Zahmatkesh, S., Pourhanasa, R., Namaghi, S.S., Klemeš, J.J., Bokhari, A. and Hajiaghaei-Keshteli, M. (2023). Multi-objective optimization of thermophysical properties GO powders-DW/EG Nf by RSM, NSGA-II, ANN, MLP and ML. Energy, 280, 128176.
https://doi.org/10.1016/j.energy.2023.128176
Boonmee, C., Arimura, M., & Asada, T. (2017). Facility location optimization model for emergency humanitarian logistics. International Journal of Disaster Risk Reduction, 24, 485-498.
https://doi.org/10.1016/j.ijdrr.2017.01.017
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.
https://doi.org/10.1016/j.ijpe.2021.108081
Ghahremani-Nahr, J., Nozari, H., & Szmelter-Jarosz, A. (2024). Designing a humanitarian relief logistics network considering the cost of deprivation using a robust-fuzzy-probabilistic planning method. Journal of International Humanitarian Action, 9(1), 19.
https://doi.org/10.1186/s41018-024-00163-8
Hemmati, A., Motevalli, S. H., Pourghader Chobar, A., Akhlaghpour, A., & Nazari, L. (2025). Analyzing customer sentiment with AI to improve the smart supply chain. Engineering Management and Soft Computing, 11(1), 306-286.
https://doi.org/10.22091/jemsc.2025.3654.1260
Izadi, E., Nikbakht, M., Feylizadeh, M., & Shahin, A. (2025). Ranking of criteria affecting humanitarian supply chain services based on blockchain platforms using multi-criteria decision-making methods. Engineering Management and Soft Computing, 10(2), 143-160.
https://doi.org/10.22091/jemsc.2025.11552.1215
Jahangiri, S., Abolghasemian, M., Ghasemi, P., & Chobar, A. P. (2023). Simulation-based optimisation: analysis of the emergency department resources under COVID-19 conditions. International journal of industrial and systems engineering, 43(1), 1-19.
https://doi.org/10.1504/IJISE.2023.128399
Kanoun, I., Chabchoub, H., & Aouni, B. (2010). Goal programming model for fire and emergency service facilities site selection. INFOR: Information Systems and Operational Research, 48(3), 143-153.
https://doi.org/10.3138/infor.48.3.143
Maharjan, R., & Hanaoka, S. (2020). A credibility-based multi-objective temporary logistics hub location-allocation model for relief supply and distribution under uncertainty. Socio-Economic Planning Sciences, 70, 100727.
https://doi.org/10.1016/j.seps.2019.07.003
Mamashli, Z., Bozorgi-Amiri, A., Dadashpour, I., Nayeri, S., & Heydari, J. (2021). A heuristic-based multi-choice goal programming for the stochastic sustainable-resilient routing-allocation problem in relief logistics. Neural Computing and Applications, 1-27.
https://doi.org/10.1007/s00521-021-06074-8
Mansoori, S., Bozorgi-Amiri, A., & Pishvaee, M. S. (2020). A robust multi-objective humanitarian relief chain network design for earthquake response, with evacuation assumption under uncertainties. Neural Computing and Applications, 32(7), 2183-2203.
https://doi.org/10.1007/s00521-019-04193-x
Narimani, R., Motamedi, M., & Amoozad khalili, H. (2023). Applying a Mathematical Model for the Distribution of Earthquake Relief Items to the Affected Areas of Tehran. Disaster Prevention and Management Knowledge. 13(2), 184-203.
http://dx.doi.org/10.32598/DMKP.13.2.747.1
Niavand, M., Adibi, M. A., & Pourghader Chobar, A. (2024). Selection of green supplier by multi-moora combination method and two-stage clustering. Engineering Management and Soft Computing, 10(1), 14-49.
https://doi.org/10.22091/jemsc.2024.10977.1181
Osmanpour, M., Eskandari, F., Nariman, N. A., & Aziz, S. Q. (2025). Stakeholder perspectives on the Official administrative and formation of water user associations in Kurdistan Province, Iran. Advances in Civil Engineering and Environmental Science, 2(1), 23-32.
https://doi.org/10.22034/acees.2025.498505.1016
Peng, D., Ye, C., & Wan, M. (2022). A multi-objective improved novel discrete particle swarm optimization for emergency resource center location problem. Engineering Applications of Artificial Intelligence, 111, 104725.
https://doi.org/10.1016/j.engappai.2022.104725
Pourghasemi, M., Rezaei, E., Ansariyar, A., Ardeshiri, A., Jeihani, M., & Tootkaboni, M. (2026). Driving behavior recognition and scoring: A Bayesian approach to driving simulator data analytics. Accident Analysis & Prevention, 232, 108523.
https://doi.org/10.1016/j.aap.2026.108523
Praneetpholkrang, P., & Huynh, V. N. (2020, February). Shelter Site Selection and Allocation Model for Efficient Response to Humanitarian Relief Logistics. In International Conference on Dynamics in Logistics (pp. 309-318). Springer, Cham.
https://doi.org/10.1007/978-3-030-44783-0_30
Rezaei Kallaj, M., Abolghasemian, M., Moradi Pirbalouti, S., Sabk Ara, M., & Pourghader Chobar, A. (2021). Vehicle Routing Problem in Relief Supply under a Crisis Condition considering Blood Types. Mathematical Problems in Engineering, 2021.
https://doi.org/10.1155/2021/7217182
Roh, S. Y., Shin, Y. R., & Seo, Y. J. (2018). The Pre-positioned warehouse location selection for international humanitarian relief logistics. The Asian Journal of Shipping and Logistics, 34(4), 297-307.
https://doi.org/10.1016/j.ajsl.2018.12.003
Sabouhi, F., Bozorgi-Amiri, A., & Vaez, P. (2020). Stochastic optimization for transportation planning in disaster relief under disruption and uncertainty. Kybernetes.
https://doi.org/10.1108/K-10-2020-0632
Shao-hong, Y., Jia-yang, N., Tai-long, C., Qiu-tong, L., Cen, Y., Jia-qing, C. & Jie, L. (2022). Location algorithm of transfer stations based on density peak and outlier detection. Applied Intelligence, 1-13.
https://doi.org/10.1007/s10489-022-03206-y
Talebian, S., Golkarieh, A., Eshraghi, S., Naseri, M., & Naseri, S. (2025). Artificial intelligence impacts on architecture and smart built environments: a comprehensive review. Advances in Civil Engineering and Environmental Science, 2(1), 45-56.
https://doi.org/10.22034/acees.2025.488106.1013
Temiz, S., Kazanç, H. C., Soysal, M., & Çimen, M. (2025). A probabilistic bi‐objective model for a humanitarian location‐routing problem under uncertain demand and road closure. International Transactions in Operational Research, 32(2), 590-625.
https://doi.org/10.1111/itor.13475
Wang, B. C., Qian, Q. Y., Gao, J. J., Tan, Z. Y., & Zhou, Y. (2021). The optimization of warehouse location and resources distribution for emergency rescue under uncertainty. Advanced Engineering Informatics, 48, 101278.
https://doi.org/10.1016/j.aei.2021.101278
Jafari, M., Akhavan, P., & Akbari, A. H. (2026). Enhancing supply chain agility and performance through big data analytics: the role of digitalization and top management support. International Journal of Productivity and Performance Management, 1-22.
https://doi.org/10.1108/IJPPM-06-2025-0557
Tavakkoli-Moghaddam, R., Akbari, A. H., Tanhaeean, M., Moghdani, R., Gholian-Jouybari, F., & Hajiaghaei-Keshteli, M. (2024). Multi-objective boxing match algorithm for multi-objective optimization problems. Expert Systems with Applications, 239, 122394.
https://doi.org/10.1016/j.eswa.2023.122394
Yavari, M., Marvi, M., & Akbari, A. H. (2020). Semi-permutation-based genetic algorithm for order acceptance and scheduling in two-stage assembly problem. Neural Computing and Applications, 32, 2989-3003.
https://doi.org/10.1007/s00521-019-04027-w
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