Zhou Yufeng, Cheng Jiahao, Wu Changzhi, Teo Kok Lay
Research Center for Economy of Upper Reaches of the Yangtze River, Chongqing Technology and Business University, Chongqing, 400067 China.
School of Business Administration, Chongqing Technology and Business University, Chongqing, 400067 China.
Complex Intell Systems. 2023 Feb 24:1-19. doi: 10.1007/s40747-023-00976-x.
The problem of blood transshipment and allocation in the context of the COVID-19 epidemic has many new characteristics, such as two-stage, trans-regional, and multi-modal transportation. Considering these new characteristics, we propose a novel multi-objective optimization model for the two-stage emergent blood transshipment-allocation. The objectives considered are to optimize the quality of transshipped blood, the satisfaction of blood demand, and the overall cost including shortage penalty. An improved integer encoded hybrid multi-objective whale optimization algorithm (MOWOA) with greedy rules is then designed to solve the model. Numerical experiments demonstrate that our two-stage model is superior to one-stage optimization methods on all objectives. The degree of improvement ranges from 0.69 to 66.26%.
在新冠疫情背景下，血液转运与分配问题呈现出诸多新特点，如分两阶段、跨区域以及多式联运等。考虑到这些新特点，我们提出了一种用于两阶段紧急血液转运-分配的新型多目标优化模型。所考虑的目标包括优化转运血液的质量、血液需求的满意度以及包括短缺惩罚在内的总成本。随后设计了一种带有贪心规则的改进整数编码混合多目标鲸鱼优化算法（MOWOA）来求解该模型。数值实验表明，我们的两阶段模型在所有目标上均优于单阶段优化方法。改进程度在0.69%至66.26%之间。
