This research introduces a blood distribution system under vendor-managed inventory that considers uncertain supply and demand. We present it as the Blood Stochastic Inventory Routing Problem, formulating it as a two-stage stochastic programming model. To solve this problem, this study proposes a three-stage matheuristic that combines a perturbation heuristic, Adaptive Large Neighborhood Search, and an exact approach. From historical data of Surabaya Blood Center in Indonesia, six sets of new instances are generated under different settings. Computational results show that our proposed three-stage matheuristic outperforms CPLEX and a two-stage matheuristic by gaining optimal or better solutions within a significantly shorter computational time. Moreover, it is robust for solving large problems, as evidenced by its ability to find high-quality solutions within a reasonable time. Finally, managerial insights are derived by evaluating performance matrices under different uncertainty levels and scenarios. According to these insights, some practical strategies are suggested with respect to the decision-maker’s risk preferences and demand characteristics.

中文翻译：

---

血液随机清单路由问题的三阶段数学

本研究介绍了供应商管理库存下的血液分配系统，该系统考虑了不确定的供需情况。我们将其表示为 Blood Stochastic Inventory Routing Problem，将其表述为两阶段随机规划模型。为了解决这个问题，本研究提出了一种三阶段数学方法，它结合了扰动启发式、自适应大邻域搜索和精确方法。从印度尼西亚泗水血液中心的历史数据来看，在不同设置下生成了 6 组新实例。计算结果表明，我们提出的三阶段数学算法在显著缩短的计算时间内获得最优或更好的解决方案，从而优于 CPLEX 和两阶段数学算法。此外，它对于解决大型问题非常强大，它能够在合理的时间内找到高质量的解决方案就证明了这一点。最后，通过评估不同不确定性水平和情景下的绩效矩阵来得出管理见解。根据这些见解，针对决策者的风险偏好和需求特征提出了一些实用的策略。