• Ala, A.,
• Simic, V.,
• Bacanin, N.,
• Tirkolaee, E.B.
• View Correspondence (jump link)

• ^aDepartment of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
• ^bFaculty of Engineering and Quantity Surveying, INTI International University, Nilai, Malaysia
• ^cUniversity of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, Belgrade, 11010, Serbia
• ^dYuan Ze University, College of Engineering, Department of Industrial Engineering and Management, Taoyuan City, 320315, Taiwan
• ^eFaculty of Informatics and Computing, Singidunum University, Danijelova 32, Belgrade, 11000, Serbia
• ^fDepartment of Industrial Engineering, Istinye University, Istanbul, Turkey
• ^gDepartment of Industrial and Mechanical Engineering, Lebanese American University, Byblos, Lebanon
• ^hMEU Research Unit, Middle East University, Amman, Jordan

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• 1

  Designing BSC enables institutional and operational evaluations, including blood donation and blood production. Shishebori and Babadi (2015) identified blood donations and distributed the network functions. They developed a bi-objective model of supply chain network difficulties. Their research included blood banks, screening, and processing facilities, donating locations, and demanding points. Ala et al. (2024) presented a novel optimization model to optimize multiple objectives, including minimizing the total costs and environmental effects in the healthcare supply chain while maximizing the social factors by developing appointments simultaneously. Şahinyazan et al. (2015) proposed a multi-objective resilient method for the multi-echelon BSC in disaster response settings to reduce costs and delivery time. They put their formulations to the test by applying them to a real-world scenario. Liu et al. (2019) utilized the discrete-event simulation framework to enhance the operation of BSC, decreasing costs and raising safety. Pirabán et al. (2019) employed a procedure based on two-stage stochastic programming to support blood inventory management within hospitals. Salehi et al. (2019) designed a stochastic programming approach with two bi-objective stages and considered blood group comparability when managing a red blood cell supply chain. Abdolazimi et al. (2023) utilized dynamic programming to address their model, employing a heuristic to estimate the forthcoming cost of a decision. In this estimation, they assumed the absence of any transshipment activities.