blood supply chain Design network based on the national blood supply network, reliability, and prioritization of the use of compatible blood groups using NSGA-II and MOICA | ||
| Journal of Engineering Management and Soft Computing | ||
| مقاله 7، دوره 11، شماره 2 - شماره پیاپی 21، اسفند 2025، صفحه 142-177 اصل مقاله (1.48 M) | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22091/jemsc.2025.12368.1258 | ||
| نویسندگان | ||
| Amir Hossein Doulatyari1؛ Parvaneh Samouei* 2 | ||
| 1Assistance Professor., Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran | ||
| 2Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran | ||
| چکیده | ||
| Disasters, especially earthquakes, have undesirable consequences such as destruction, loss of life and weakening of the effectiveness of health services, and a significant challenge after a devastating earthquake is how to provide blood to the affected people in hospitals. The aim of this study is to present a multi-objective mathematical model in order to reduce the cost and increase the reliability of the blood supply chain. Reliability is considered to factors such as transportation conditions, road damage, temperature fluctuations, packaging standards, laboratory equipment. In this model, the amount of blood collected from donors, the number and location of blood collection centers, the amount of blood in each center and hospital are considered. Also, considering the past experiences of crisis management organizations and the Red Crescent, in order to get closer to real-world issues, different blood groups, compatibility between some blood groups and the use of the national blood supply network for the use of certain provinces have been considered. Also, in this study, a policy is considered to encourage blood donors... | ||
| کلیدواژهها | ||
| Encouragement in the blood supply chain؛ national blood supply network؛ prioritization؛ blood group compatibility؛ reliability | ||
| مراجع | ||
|
Arani, M., Momenitabar, M., Ebrahimi, Z. D., & Liu, X. (2021). A Two-Stage Stochastic Programming Model for Blood Supply Chain Management, Considering Facility Disruption and Service Level. arXiv preprint arXiv:2111.02808. https://doi.org/10.48550/arXiv.2111.02808 Arani, M., Chan, Y., Liu, X., & Momenitabar, M. (2021). A lateral resupply blood supply chain network design under uncertainties. Applied mathematical modelling, 93, 165-187. https://doi.org/10.1016/j.apm.2020.12.010 Behroozi, F., Monfared, M. A. S., & Hosseini, S. M. H. (2021). Investigating the conflicts between different stakeholders’ preferences in a blood supply chain at emergencies: a trade-off between six objectives. Soft Computing, 25(21), 13389-13410. https://doi.org/10.1007/s00500-021-06157-7 Deb, K., Sindhya, K., & Hakanen, J. (2016). Multi-objective optimization. In Decision sciences (pp. 161-200). CRC Press. Fahmihassan, A., Moghari, M., & Ebadati, O. (2020). Prediction of Blood Donations Using Data Mining Based on the Decision Tree Algorithms KNN, SVM, and MLP. Engineering Management and Soft Computing, 6(1), 109-129. doi: 10.22091/jemsc.2018.1278. Farrokhizadeh, E., Seyfi-Shishavan, S. A., & Satoglu, S. I. (2022). Blood supply planning during natural disasters under uncertainty: a novel bi-objective model and an application for red crescent. Annals of Operations Research, 319(1), 73-113. https://doi.org/10.1007/s10479-021-03978-5 Farshidi, Y., Ghasemi, R., & Sharafian Ardekani, A. (2022). Designing a Neural Observer to Estimate the State Variables of the Dynamical System of a Specific Class of Leukaemia. Engineering Management and Soft Computing, 7(2), 124-144. doi: 10.22091/jemsc.2018.1000.1041 Fazli-Khalaf, M., Khalilpourazari, S., & Mohammadi, M. (2019). Mixed robust possibilistic flexible chance constraint optimization model for emergency blood supply chain network design. Annals of operations research, 283, 1079-1109. https://doi.org/10.1007/s10479-017-2729-3 Ghorashi, S. B., Hamedi, M., & Sadeghian, R. (2020). Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural computing and applications, 32, 12173-12200. https://doi.org/10.1007/s00521-019-04343-1 Hosseini, S. M. H., Behroozi, F., & Sana, S. S. (2023). Multi-objective optimization model for blood supply chain network design considering cost of shortage and substitution in disaster. RAIRO-Operations Research, 57(1), 59-85. https://doi.org/10.1051/ro/2022206 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, 1085-1104. https://doi.org/10.1007/s12652-019-01315-0 Hosseini-Motlagh, S. M., Samani, M. R. G., & Cheraghi, S. (2020). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-economic planning sciences, 70, 100725. https://doi.org/10.1016/j.seps.2019.07.001 Hosseini-Motlagh, S. M., Samani, M. R. G., & Homaei, S. (2020). Toward a coordination of inventory and distribution schedules for blood in disasters. Socio-Economic Planning Sciences, 72, 100897. https://doi.org/10.1016/j.seps.2020.100897
Khalilpourazari, S., & Arshadi Khamseh, A. (2019). Bi-objective emergency blood supply chain network design in earthquake considering earthquake magnitude: a comprehensive study with real world application. Annals of Operations Research, 283, 355-393. https://doi.org/10.1007/s10479-017-2588-y Khalilpourazari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: neural learning, optimization and case study. Annals of Operations Research, 289, 123-152. https://doi.org/10.1007/s10479-019-03437-2 Moslemi, S., & Pasandideh, S. H. R. (2021). A location-allocation model for quality-based blood supply chain under IER uncertainty. RAIRO-operations research, 55, S967-S998. https://doi.org/10.1051/ro/2020035 Namazian, A. and Babazadeh, R. (2025). Designing supply chain of blood under uncertainty: A case study. International Journal of Research in Industrial Engineering, 14(1), 177-195. doi: 10.22105/riej.2024.436665.1415 Nahofti Kohneh, J., Derikvand, H., Amirdadi, M., & Teimoury, E. (2023). A blood supply chain network design with interconnected and motivational strategies: A case study. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8249-8269. https://doi.org/10.1007/s12652-021-03594-y Rashidzadeh, E., Hadji Molana, S. M., Soltani, R., & Hafezalkotob, A. (2021). Assessing the sustainability of using drone technology for last-mile delivery in a blood supply chain. Journal of Modelling in Management, 16(4), 1376-1402. https://doi.org/10.1108/JM2-09-2020-0241 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, 1-10. https://doi.org/10.1155/2021/7217182 Razavi, N., Gholizadeh, H., Nayeri, S., & Ashrafi, T. A. (2021). A robust optimization model of the field hospitals in the sustainable blood supply chain in crisis logistics. Journal of the Operational Research Society, 72(12), 2804-2828. https://doi.org/10.1080/01605682.2020.1821586 Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2019). Developing a robust stochastic model for designing a blood supply chain network in a crisis: a possible earthquake in Tehran. Annals of operations research, 283, 679-703. https://doi.org/10.1007/s10479-017-2533-0 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(1-2), 1413-1462. https://doi.org/10.1007/s10479-018-2873-4 Seyfi-Shishavan, S. A., Donyatalab, Y., Farrokhizadeh, E., & Satoglu, S. I. (2023). A fuzzy optimization model for designing an efficient blood supply chain network under uncertainty and disruption. Annals of operations research, 331(1), 447-501. https://doi.org/10.1007/s10479-021-04123-y Yang, H., Yin, Y., Wang, D., Cheng, T. C. E., Zhang, R., & Hu, H. (2024). An integrated blood supply chain network design during a pandemic. International Journal of Production Research, 63(9), 3384–3409. https://doi.org/10.1080/00207543.2024.2396511 Yousefi Nejad, M., Khayat Rasouli, M., & Khalilpour, Z. (2022). Optimizing Red Blood Cell Consumption Using Markov's Decision-Making Process (Case study: Blood Bank of Zanjan Province Blood Transfusion Center). Engineering Management and Soft Computing, 8(1), 71-84. doi: 10.22091/jemsc.2019.1296 Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & Da Fonseca, V. G. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE Transactions on evolutionary computation, 7(2), 117-132. https://doi.org/10.1109/TEVC.2003.810758 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 Tanhaeean, M., Tavakkoli-Moghaddam, R., & Akbari, A. H. (2022). Boxing match algorithm: A new meta-heuristic algorithm. Soft Computing, 26(24), 13277-13299. https://doi.org/10.1007/s00500-022-07518-6 Akbari, A. H., Jafari, M., & Akhavan, P. (2025). Deep Reinforcement Learning Algorithm: Dynamic Job Shop Scheduling Problem with Order Rejection and Inventory. Journal of Advanced Manufacturing Systems. https://doi.org/10.1142/S0219686727500156 | ||
|
آمار تعداد مشاهده مقاله: 200 تعداد دریافت فایل اصل مقاله: 68 |
||