طراحی شبکه زنجیره تامین خون با توجه به شبکه ملی خونرسانی، قابلیت اطمینان و اولویت بندی استفاده از گروه های خونی سازگار به کمکNSGA-II و MOICA

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، دپارتمان مهندسی صنایع، دانشکده مهندسی، دانشگاه بوعلی سینا، همدان، ایران

2 استادیار، گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه بوعلی سینا، همدان، ایران

چکیده

بلایا، به‌ویژه زلزله، عواقب نامطلوبی مانند تخریب، ازدست‌دادن جان انسان‌ها و تضعیف اثربخشی خدمات بهداشتی را به همراه دارد و یک چالش قابل‌توجه پس از زلزله ویرانگر نحوه تهیه خون به افراد آسیب‌دیده در بیمارستان‌ها است. هدف از انجام این مطالعه ارائه یک مدل ریاضی چندهدفه در راستای کاهش هزینه و افزایش قابلیت اطمینان زنجیره تأمین خون است. قابلیت اطمینان به عواملی نظیر شرایط حمل، خرابی جاده، نوسانات دمایی، استاندارهای بسته‌بندی، تجهیزات آزمایشگاهی توجه شده است. در این مدل میزان خون جمع‌آوری‌شده از اهداکنندگان، تعداد و مکان مراکز جمع‌آوری خون، میزان خون در هر مرکز و بیمارستان لحاظ می‌شود. همچنین باتوجه‌به تجربیات گذشته سازمان‌های مدیریت بحران و هلال‌احمر، برای نزدیک‌تر شدن به مسائل دنیای واقعی، گروه‌های خونی مختلف، سازگاری بین برخی از گروه‌های خونی و استفاده از شبکه ملی خون‌رسانی جهت استفاده از استان‌های معین منظور شده است. همچنین در این مطالعه، سیاستی هم برای تشویق اهداکنندگان خون در نظر گرفته می‌شود. به‌علاوه به دلیل عدم اتلاف زمان و نجات جان افراد آسیب‌دیده به‌خصوص در ساعات اولیه بحران، انتقال خون به مراکز سانحه‌دیده هم ...

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

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

نویسندگان [English]

  • Amir Hossein Doulatyari 1
  • Parvaneh Samouei 2
1 Assistance Professor., Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
2 Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran
چکیده [English]

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 [English]

  • Encouragement in the blood supply chain
  • national blood supply network
  • prioritization
  • blood group compatibility
  • reliability
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 Computing6(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 Computing7(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 Engineering14(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
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