Abbas, H., Zhao, L., Gong, X., & Faiz, N. (2023). The perishable products case to achieve sustainable food quality and safety goals implementing on-field sustainable supply chain model. Socio-Economic Planning Sciences, 87, 101562. https://doi.org/10.1016/j.seps.2023.101562.

Abdelmaboud, A., Jawawi, D. N., Ghani, I., Elsafi, A., & Kitchenham, B. (2015). Quality of service approaches in cloud computing: A systematic mapping study. Journal of Systems and Software, 101, 159-179. https://doi.org/10.1016/j.jss.2014.12.015.

Alajmi, Q., Sadiq, A., Kamaludin, A., & Al-Sharafi, M. A. (2017, May). E-learning models: The effectiveness of the cloud-based E-learning model over the traditional E-learning model. In 2017 8th International Conference on Information Technology (ICIT) (pp. 12-16). IEEE. DOI: 10.1109/ICITECH.2017.8079909.

Ali, O., & Osmanaj, V. (2020). The role of government regulations in the adoption of cloud computing: A case study of local government. Computer Law & Security Review, 36, 105396. https://doi.org/10.1016/j.clsr.2020.105396.

Aminullah, E., & Erman, E. (2021). Policy innovation and emergence of innovative health technology: The system dynamics modelling of early COVID-19 handling in Indonesia. Technology in Society, 66, 101682. https://doi.org/10.1016/j.techsoc.2021.101682.

Battarra, I., Accorsi, R., Lupi, G., Manzini, R., & Sirri, G. (2022). Location-allocation problem in a multi-terminal cross-dock distribution network for palletized perishables delivery. Transportation Research Procedia, 67, 172-181. https://doi.org/10.1016/j.trpro.2022.12.048.

Beliën, J., & Forcé, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16. https://doi.org/10.1016/j.ejor.2011.05.026.

Ben Elmir, W., Hemmak, A., & Senouci, B. (2023). Smart platform for data blood bank management: forecasting demand in blood supply chain using machine learning. Information, 14(1), 31. https://doi.org/10.3390/info14010031.

Budak, A., & Çoban, V. (2021). Evaluation of the impact of blockchain technology on supply chain using cognitive maps. Expert Systems with Applications, 184, 115455. https://doi.org/10.1016/j.eswa.2021.115455.

Cassidy, R., Singh, N. S., Schiratti, P. R., Semwanga, A., Binyaruka, P., Sachingongu, N., ... & Blanchet, K. (2019). Mathematical modelling for health systems research: a systematic review of system dynamics and agent-based models. BMC Health Services Research, 19, 1-24. https://doi.org/10.1186/s12913-019-4627-7

Chang, S. C., Lu, M. T., Pan, T. H., & Chen, C. S. (2021). Evaluating the E-health cloud computing systems adoption in Taiwan’s healthcare industry. Life, 11(4), 310. https://doi.org/10.3390/life11040310.

Cresswell, K., Domínguez Hernández, A., Williams, R., & Sheikh, A. (2022). Key challenges and opportunities for cloud technology in health care: Semistructured interview study. JMIR Human Factors, 9(1), e31246. doi:10.2196/31246.

Damtew, A. W., Borena, T., & Yilma, Y. (2021). The roles of cloud-based supply chain integration on firm performances and competitiveness. International Journal of Industrial and Manufacturing Systems Engineering, 6(3), 49-58. doi: 10.11648/j.ijimse.20210603.12.

Ding, Z., Gong, W., Li, S., & Wu, Z. (2018). System dynamics versus agent-based modeling: A review of complexity simulation in construction waste management. Sustainability, 10(7), 2484. https://doi.org/10.3390/su10072484.

Duan, Q., & Liao, T. W. (2014). Optimization of blood supply chain with shortened shelf lives and ABO compatibility. International Journal of Production Economics, 153, 113-129. https://doi.org/10.1016/j.ijpe.2014.02.012.

Duong, L. N. K., Wood, L. C., & Wang, W. Y. C. (2020). Inventory management of perishable health products: a decision framework with non-financial measures. Industrial Management & Data Systems, 120(5), 987-1002. https://doi.org/10.1108/IMDS-11-2019-0594.

Dural Selcuk, G., & Vasilakis, C. (2023). Evaluating the sustainability of complex health system transformation in the context of population ageing: An empirical system dynamics study. Journal of the Operational Research Society, 74(1), 1-17. https://doi.org/10.1080/01605682.2021.1992307.

Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Amin, S. H. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236-250. https://doi.org/10.1016/j.engappai.2018.03.004.

Eslami, M. H., Jafari, H., Achtenhagen, L., Carlbäck, J., & Wong, A. (2024). Financial performance and supply chain dynamic capabilities: the Moderating Role of Industry 4.0 technologies. International Journal of Production Research, 62(22), 8092-8109. https://doi.org/10.1080/00207543.2021.1966850.

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. https://doi.org/10.1016/j.ijpe.2015.11.007.

Fahmy, S. A., Zaki, A. M., & Gaber, Y. H. (2023). Optimal locations and flow allocations for aggregation hubs in supply chain networks of perishable products. Socio-Economic Planning Sciences, 86, 101500. https://doi.org/10.1016/j.seps.2022.101500.

Farid, M., Purdy, N., & Neumann, W. P. (2020). Using system dynamics modelling to show the effect of nurse workload on nurses’ health and quality of care. Ergonomics, 63(8), 952-964. https://doi.org/10.1080/00140139.2019.1690674.

Ferguson, M., & Ketzenberg, M. E. (2006). Information sharing to improve retail product freshness of perishables. Production and Operations Management, 15(1), 57-73. https://doi.org/10.1111/j.1937-5956.2006.tb00003.x.

Forrester, J. W. (1958). Industrial dynamics: a major breakthrough for decision makers. Harvard Business Review, 36(4), 37-66. DOI: 10.1225/58404.

Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), 669-686. https://doi.org/10.1108/JMTM-10-2019-0368.

Gao, F., & Sunyaev, A. (2019). Context matters: A review of the determinant factors in the decision to adopt cloud computing in healthcare. International Journal of Information Management, 48, 120-138. https://doi.org/10.1016/j.ijinfomgt.2019.02.002.

German, J. D., Mina, J. K. P., Alfonso, C. M. N., & Yang, K. H. (2018, April). A study on shortage of hospital beds in the Philippines using system dynamics. In 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), IEEE, 72-78. DOI: 10.1109/IEA.2018.8387073.

Gharehbaghian, A., Abolghasemi, H., & Namini, M. T. (2008). Status of blood transfusion services in Iran. Asian Journal of Transfusion Science, 2(1), 13-17. DOI: 10.4103/0973-6247.39505.

Ghasemi, P., Khalili, H. A., Chobar, A. P., Safavi, S., & Hejri, F. M. (2022). A New Multiechelon Mathematical Modeling for Pre‐and Postdisaster Blood Supply Chain: Robust Optimization Approach. Discrete Dynamics in Nature and Society, 2022(1), 2976929. https://doi.org/10.1155/2022/2976929.

Ghasemzadeh, F., & Pamucar, D. (2023). A local supply chain inventory planning with varying perishability rate product: A case study. Expert Systems with Applications, 215, 119362. https://doi.org/10.1016/j.eswa.2022.119362.

Golestani, M., Moosavirad, S. H., Asadi, Y., & Biglari, S. (2021). A multi-objective green hub location problem with multi item-multi temperature joint distribution for perishable products in cold supply chain. Sustainable Production and Consumption, 27, 1183-1194. https://doi.org/10.1016/j.spc.2021.02.026.

Habibi-Kouchaksaraei, M., Paydar, M. M., & Asadi-Gangraj, E. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling, 55, 583-599. https://doi.org/10.1016/j.apm.2017.11.004.

Hald, K. S., & Kinra, A. (2019). How the blockchain enables and constrains supply chain performance. International Journal of Physical Distribution & Logistics Management, 49(4), 376-397. https://doi.org/10.1108/IJPDLM-02-2019-0063.

Hamdan, B., & Diabat, A. (2019). A two-stage multi-echelon stochastic blood supply chain problem. Computers & Operations Research, 101, 130-143. https://doi.org/10.1016/j.cor.2018.09.001.

Hashemi-Amiri, O., Ghorbani, F., & Ji, R. (2023). Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach. Computers & Industrial Engineering, 175, 108845. https://doi.org/10.1016/j.cie.2022.108845.

Heidari-Fathian, H., & Pasandideh, S. H. R. (2018). Green-blood supply chain network design: Robust optimization, bounded objective function & Lagrangian relaxation. Computers & Industrial Engineering, 122, 95-105. https://doi.org/10.1016/j.cie.2018.05.051.

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.

Hosseinifard, Z., & Abbasi, B. (2018). The inventory centralization impacts on sustainability of the blood supply chain. Computers & Operations Research, 89, 206-212. https://doi.org/10.1016/j.cor.2016.08.014

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.

Idoga, P. E., Toycan, M., Nadiri, H., & Çelebi, E. (2019). Assessing factors militating against the acceptance and successful implementation of a cloud based health center from the healthcare professionals’ perspective: a survey of hospitals in Benue state, northcentral Nigeria. BMC Medical Informatics and Decision Making, 19, 1-18. https://doi.org/10.1186/s12911-019-0751-x.

Jaigirdar, S. M., Das, S., Chowdhury, A. R., Ahmed, S., & Chakrabortty, R. K. (2023). Multi-objective multi-echelon distribution planning for perishable goods supply chain: A case study. International Journal of Systems Science: Operations & Logistics, 10(1), 2020367. https://doi.org/10.1080/23302674.2021.2020367

Javaid, M., Haleem, A., Singh, R. P., Rab, S., Suman, R., & Khan, I. H. (2022). Evolutionary trends in progressive cloud computing based healthcare: Ideas, enablers, and barriers. International Journal of Cognitive Computing in Engineering, 3, 124-135. https://doi.org/10.1016/j.ijcce.2022.06.001.

Jelassi, M., Ghazel, C., & Saïdane, L. A. (2017, September). A survey on quality of service in cloud computing. In 2017 3rd International Conference on Frontiers of Signal Processing (ICFSP) (pp. 63-67). IEEE. DOI: 10.1109/ICFSP.2017.8097142

Jhang-Li, J. H., & Chiang, I. R. (2015). Resource allocation and revenue optimization for cloud service providers. Decision Support Systems, 77, 55-66. https://doi.org/10.1016/j.dss.2015.04.008.

Katsaliaki, K., & Brailsford, S. C. (2007). Using simulation to improve the blood supply chain. Journal of The Operational Research Society, 58(2), 219-227. https://doi.org/10.1057/palgrave.jors.2602195.

Kaur, P. D., & Chana, I. (2014). Cloud based intelligent system for delivering health care as a service. Computer Methods and Programs in Biomedicine, 113(1), 346-359. https://doi.org/10.1016/j.cmpb.2013.09.013.

Kendall, K. E., & Lee, S. M. (1980). Formulating blood rotation policies with multiple objectives. Management Science, 26(11), 1145-1157. https://doi.org/10.1287/mnsc.26.11.1145.

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.

Kim, S., Kim, J., & Kim, D. (2020). Implementation of a blood cold chain system using blockchain technology. Applied Sciences, 10(9), 3330. https://doi.org/10.3390/app10093330.

Krishnan, R., Arshinder, K., & Agarwal, R. (2022). Robust optimization of sustainable food supply chain network considering food waste valorization and supply uncertainty. Computers & Industrial Engineering, 171, 108499. https://doi.org/10.1016/j.cie.2022.108499.

Larimi, N. G., & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & Industrial Engineering, 137, 106014. https://doi.org/10.1016/j.cie.2019.106014.

Legenvre, H., Henke, M., & Ruile, H. (2020). Making sense of the impact of the internet of things on Purchasing and Supply Management: A tension perspective. Journal of Purchasing and Supply Management, 26(1), 100596. https://doi.org/10.1016/j.pursup.2019.100596.

Lin, A., & Chen, N. C. (2012). Cloud computing as an innovation: Percepetion, attitude, and adoption. International Journal of Information Management, 32(6), 533-540. https://doi.org/10.1016/j.ijinfomgt.2012.04.001.

Links, J. M., Schwartz, B. S., Lin, S., Kanarek, N., Mitrani-Reiser, J., Sell, T. K., ... & Kendra, J. M. (2018). COPEWELL: a conceptual framework and system dynamics model for predicting community functioning and resilience after disasters. Disaster Medicine and Public Health Preparedness, 12(1), 127-137. https://doi.org/10.1017/dmp.2017.39.

Lu, Q., Chen, J., Song, H., & Zhou, X. (2022). Effects of cloud computing assimilation on supply chain financing risks of SMEs. Journal of Enterprise Information Management, 35(6), 1719-1741. https://doi.org/10.1108/JEIM-11-2020-0461.

Mansur, A., Setiawan, N., Faiz, A. H., & Indrawati, S. (2024). Improving Blood Donations and Lean Blood Bank Services in Indonesian Red Cross: A System Dynamics Approach. Mathematical Modelling of Engineering Problems, 11(9). https://doi.org/10.18280/mmep.110918.

Mansur, A., Vanany, I., & Arvitrida, N. I. (2018). Challenge and opportunity research in blood supply chain management: a literature review. In MATEC Web of Conferences (Vol. 154, p. 01092). EDP Sciences. https://doi.org/10.1051/matecconf/201815401092.

Maresova, P., Sobeslav, V., & Krejcar, O. (2017). Cost–benefit analysis–evaluation model of cloud computing deployment for use in companies. Applied Economics, 49(6), 521-533. https://doi.org/10.1080/00036846.2016.1200188.

Mashat, R. M., Abourokbah, S. H., & Salam, M. A. (2024). Impact of Internet of Things Adoption on Organizational Performance: A Mediating Analysis of Supply Chain Integration, Performance, and Competitive Advantage. Sustainability, 16(6), 2250. https://doi.org/10.3390/su16062250.

Mohammadi, Z., Barzinpour, F., & Teimoury, E. (2023). A location-inventory model for the sustainable supply chain of perishable products based on pricing and replenishment decisions: A case study. PloS One, 18(7), e0288915. https://doi.org/10.1371/journal.pone.0288915.

Murmu, V., Kumar, D., Sarkar, B., Mor, R. S., & Jha, A. K. (2023). Sustainable inventory management based on environmental policies for the perishable products under first or last in and first out policy. Journal of Industrial and Management Optimization, 19(7), 4764-4803. Doi: 10.3934/jimo.2022149.

Nahmias, S. (1982). Perishable inventory theory: A review. Operations Research, 30(4), 680-708. https://doi.org/10.1287/opre.30.4.680.

Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2015). A structured review of quantitative models in the blood supply chain: a taxonomic framework for decision-making. International Journal of Production Research, 53(24), 7191-7212. https://doi.org/10.1080/00207543.2015.1005766.

Osorio, A. F., Brailsford, S. C., Smith, H. K., Forero-Matiz, S. P., & Camacho-Rodríguez, B. A. (2017). Simulation-optimization model for production planning in the blood supply chain. Health Care Management Science, 20, 548-564. https://doi.org/10.1007/s10729-016-9370-6.

Paul, T., Mondal, S., Islam, N., & Rakshit, S. (2021). The impact of blockchain technology on the tea supply chain and its sustainable performance. Technological Forecasting and Social Change, 173, 121163. https://doi.org/10.1016/j.techfore.2021.121163.

Püschel, T., Schryen, G., Hristova, D., & Neumann, D. (2015). Revenue management for cloud computing providers: Decision models for service admission control under non-probabilistic uncertainty. European Journal of Operational Research, 244(2), 637-647. https://doi.org/10.1016/j.ejor.2015.01.027.

Quynh, N. T. T., Son, H. X., Le, T. H., Huy, H. N. D., Vo, K. H., Luong, H. H., ... & Duong-Trung, N. (2021). Toward a design of blood donation management by blockchain technologies. In Computational Science and Its Applications–ICCSA 2021: 21st International Conference, Cagliari, Italy, September 13–16, 2021, Proceedings, Part VIII 21 (pp. 78-90). Springer International Publishing. https://doi.org/10.1007/978-3-030-87010-2_6.

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. https://doi.org/10.1016/j.tre.2017.06.004.

Rashid, A., Rasheed, R., Ngah, A. H., & Amirah, N. A. (2024). Unleashing the power of cloud adoption and artificial intelligence in optimizing resilience and sustainable manufacturing supply chain in the USA. Journal of Manufacturing Technology Management, (ahead-of-print). https://doi.org/10.1108/JMTM-02-2024-0080.

Ratten, V. (2012). Entrepreneurial and ethical adoption behaviour of cloud computing. The Journal of High Technology Management Research, 23(2), 155-164. https://doi.org/10.1016/j.hitech.2012.06.006.

Rosati, P., Fox, G., Kenny, D., & Lynn, T. (2017, December). Quantifying the financial value of cloud investments: a systematic literature review. In 2017 IEEE international conference on cloud computing technology and science (CloudCom) (pp. 194-201). IEEE. DOI: 10.1109/CloudCom.2017.28.

Sadri, S., Shahzad, A., & Zhang, K. (2021, February). Blockchain traceability in healthcare: Blood donation supply chain. In 2021 23rd International Conference on Advanced Communication Technology (ICACT) (pp. 119-126). IEEE. DOI: 10.23919/ICACT51234.2021.9370704.

Safari, F., Safari, N., Hasanzadeh, A., & Ghatari, A. R. (2015). Factors affecting the adoption of cloud computing in small and medium enterprises. International Journal of Business Information Systems, 20(1), 116-137. https://doi.org/10.1504/IJBIS.2015.070894.

Sahin, M., Ko, H. S., Lee, H. F., & Azambuja, M. (2017). A simulation case study on supply chain management of a construction firm adopting cloud computing and RFID. International Journal of Industrial and Systems Engineering, 27(2), 233-254. https://doi.org/10.1504/IJISE.2017.086269.

Sallehudin, H., Aman, A. H. M., Razak, R. C., Ismail, M., Bakar, N. A. A., Fadzil, A. F. M., & Baker, R. (2020). Performance and key factors of cloud computing implementation in the public sector. International Journal of Business and Society, 21(1), 134-152. https://doi.org/10.33736/ijbs.3231.2020.

Savadkoohi, E., Mousazadeh, M., & Torabi, S. A. (2018). A possibilistic location-inventory model for multi-period perishable pharmaceutical supply chain network design. Chemical Engineering Research and Design, 138, 490-505. https://doi.org/10.1016/j.cherd.2018.09.008.

Shirzad Talatappeh, S., & Lakzi, A. (2020). Developing a model for investigating the impact of cloud-based systems on green supply chain management. Journal of Engineering, Design and Technology, 18(4), 741-760. https://doi.org/10.1108/JEDT-06-2019-0161.

Silbermayr, L., & Waitz, M. (2024). Omni-channel inventory management of perishable products under transshipment and substitution. International Journal of Production Economics, 267, 109089. https://doi.org/10.1016/j.ijpe.2023.109089.

Stranieri, S., Riccardi, F., Meuwissen, M. P., & Soregaroli, C. (2021). Exploring the impact of blockchain on the performance of agri-food supply chains. Food Control, 119, 107495. https://doi.org/10.1016/j.foodcont.2020.107495.

Sy, C., Bernardo, E., Miguel, A., San Juan, J. L., Mayol, A. P., Ching, P. M., ... & Mutuc, J. E. (2020). Policy development for pandemic response using system dynamics: a case study on COVID-19. Process Integration and Optimization for Sustainability, 4, 497-501. https://doi.org/10.1007/s41660-020-00130-x.

Tirkolaee, E. B., Golpîra, H., Javanmardan, A., & Maihami, R. (2023). A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study. Socio-Economic Planning Sciences, 85, 101439. https://doi.org/10.1016/j.seps.2022.101439.

Tirkolaee, E. B., Hadian, S., Weber, G. W., & Mahdavi, I. (2020). A robust green traffic‐based routing problem for perishable products distribution. Computational Intelligence, 36(1), 80-101. https://doi.org/10.1111/coin.12240.

Vanany, I., Maryani, A., Amaliah, B., Rinaldy, F., & Muhammad, F. (2015). Blood traceability system for Indonesian blood supply chain. Procedia Manufacturing, 4, 535-542. https://doi.org/10.1016/j.promfg.2015.11.073.

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

Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2020). Dynamics between blockchain adoption determinants and supply chain performance: An empirical investigation. International Journal of Production Economics, 229, 107791. https://doi.org/10.1016/j.ijpe.2020.107791.

Yavari, M., & Geraeli, M. (2019). Heuristic method for robust optimization model for green closed-loop supply chain network design of perishable goods. Journal of Cleaner Production, 226, 282-305. https://doi.org/10.1016/j.jclepro.2019.03.279.

Yavari, M., & Zaker, H. (2019). An integrated two-layer network model for designing a resilient green-closed loop supply chain of perishable products under disruption. Journal of Cleaner Production, 230, 198-218. https://doi.org/10.1016/j.jclepro.2019.04.130.

Yavari, M., & Akbari, A. H. (2023). Service level and profit maximisation in order acceptance and scheduling problem with weighted tardiness. International Journal of Industrial and Systems Engineering, 43(3), 331-362. https://doi.org/10.1504/IJISE.2023.129138

Akbari, A. H., & Jafari, M. (2025). Development of a Deep Reinforcement Learning Algorithm in a Dynamic Cellular Manufacturing System Considering Order Rejection, Case Study: Stone Paper Factory. Engineering Management and Soft Computing, 10(2), 204-222. doi: 10.22091/jemsc.2025.11853.1230

Jabbari, M., Rezaeenour, J., & Akbari, A. H. (2023). A Feature Selection Method Based on Information Theory and Genetic Algorithm. Sciences and Techniques of Information Management, 9(3), 32-7. doi: 10.22091/stim.2023.8708.1877

Zahiri, B., & Pishvaee, M. S. (2017). Blood supply chain network design considering blood group compatibility under uncertainty. International Journal of Production Research, 55(7), 2013-2033. https://doi.org/10.1080/00207543.2016.1262563.

Zhang, J., & Li, Y. (2023). Collaborative vehicle-drone distribution network optimization for perishable products in the epidemic situation. Computers & Operations Research, 149, 106039. https://doi.org/10.1016/j.cor.2022.106039.