Recently, supply chain network design has been a demanding question and attracted great interest in a wide range of fields, including the medical industry. The heart of the entire discipline of the medical industry is the concept of blood management which nowadays brought striking attention among managers and decision-makers. Thus, a bi-objective and sustainable blood supply chain network is introduced in this study by considering both social and environmental factors of blood decomposition. In addition, some aspects of uncertainty are firstly considered to the problem in terms of both the amount of gathered blood from blood transfusion centers and the decomposition ratio in the blood decomposition center. Considering bi-objective programming in the developed network, the problem simultaneously considers optimizing the ${\mathit{CO}}_2$ emission, balancing the flow of all utilized vehicles, penalty coefficient of non-visited centers, total costs of using all vehicles, and more importantly the social and environmental factors of blood decomposition. Therefore, the total cost is minimized by the first objective function along with the environmental factor of greenhouse gas emission while maximizing the social factor of blood decomposition is done by the second objective function which gathers and produces the maximum amount of blood sub-products for patients using stochastic programming. Several sensitivity analyses are conducted for a better implication of the problem in various conditions. The findings of the study suggest that expecting more social factors convey more costs in most cases. Therefore, blood supply chain managers must adjust their aims and scope to achieve the best-desired results for society.

最近，供应链网络设计一直是一个棘手的问题，引起了包括医疗行业在内的广泛领域的极大兴趣。医疗行业整个学科的核心是血液管理的概念，如今它引起了管理人员和决策者的极大关注。因此，通过考虑血液分解的社会和环境因素，本研究引入了一个双目标和可持续的血液供应链网络。此外，首先从输血中心采集的血液量和血液分解中心的分解率两方面考虑不确定性的一些方面。考虑到开发网络中的双目标规划，该问题同时考虑优化${\mathit{二氧化碳}}_2$排放，平衡所有使用车辆的流量，非访问中心的惩罚系数，使用所有车辆的总成本，更重要的是血液分解的社会和环境因素。因此，总成本由第一个目标函数连同温室气体排放的环境因素一起最小化，而血液分解的社会因素最大化由第二个目标函数完成，为患者收集和生产最大量的血液子产品。使用随机编程。为了更好地说明问题在各种条件下的含义，我们进行了多项敏感性分析。研究结果表明，在大多数情况下，期望更多的社会因素会带来更多的成本。所以，