基于改进鲸鱼优化算法的血液供应链网络多目标鲁棒优化设计

为解决血液供应链网络设计中的不确定性问题，建立了一种血液供应链网络多目标鲁棒优化设计模型。首先，针对带有5个节点的血液供应链网络，建立考虑安全库存的、目标为成本最小、存储时间最短的优化函数，并采用ε约束、Pareto最优和鲁棒优化方法对已建模型进行处理，将多目标问题转化为单目标鲁棒问题；其次，对原有鲸鱼优化算法（WOA）进行改进，引入差分算法的交叉和变异理念，增强了搜索能力并改善了局限性，从而得到差分鲸鱼优化算法（DWOA），并采用此算法对处理后的模型求解。通过数值实例，验证当测试问题相同时，优化模型在需求短缺方面比确定模型的短缺量平均少76%。因此，所提优化模型在应对需求短缺时更具优势；通过仿真对比分析图像，得出DWOA相比WOA、粒子群优化（PSO）算法和遗传算法（GA）中断时间更短并且成本更低。

Multi-objective robust optimization design of blood supply chain network based on improved whale optimization algorithm

In order to solve the uncertainty problem of blood supply chain network design, a multi-objective robust optimization design model of blood supply chain network was established. Firstly, for the blood supply chain network with five nodes, an optimization function considering safe stock, minimum cost and shortest storage time was established, and the ε-constraint, Pareto optimization and robust optimization method were used to deal with the established model, so that the multi-objective problem was transformed into a single objective robust problem. Secondly, by improving the original Whale Optimization Algorithm (WOA), the concept of crossover and mutation of the differential algorithm was introduced to WOA to enhance the search ability and improve the limitations, so as to obtain the Differential WOA (DWOA), which was used to solve the processed model. Finally, a numerical example verified that the shortage of the robust model is 76% less than that of the deterministic model when the test problems are the same. Therefore, the optimization robust model has more advantages in dealing with demand shortage. Compared with WOA, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), DWOA has shorter interruption time and lower cost.