一、个人简介
肖久红，女，汉族，博士。2025年12月毕业于南开大学物流学专业，获经济学博士学位。研究成果发表于Transportation Research Part E:Logistics and Transportation Review、Computers & Industrial Engineering、International Journal of Computer Integrated Manufacturing、中国管理科学等国内外学术期刊，获发明专利4项。
主要研究方向：智慧物流、智能制造、智能优化算法等。
电子邮箱：xiaojh_226@163.com
二、代表性科研成果
Xiao, J. H., Wang, S. Y., Xiao, J. H., & Huang, G. Q. Delivery routing for electric vehicles withen-route mobile battery swapping.Transportation Research Part E: Logistics and Transportation Review,2024, 192, 103838.(SCI,Q1)
Xiao, J. H.,Li, Y., Cao, Z. G., & Xiao, J. H. Cooperative trucks and drones for rural last-mile delivery with steep roads.Computers & Industrial Engineering,2024, 187, 109849.(SCI,Q1)
Xiao, J. H., Cai, Y. S., & Chen, Y. Study on deep reinforcement learning for multi-task scheduling in cloud manufacturing.International Journal of Computer Integrated Manufacturing, 2025,38(12), 1663-1680.(SCI,Q2)
Xiao, J. H.,Yang, Y. F.,Xiao, J. H.*, Wang, S. Y., & Huang, G. Q. Multi-period hub-and-spoke network design considering flow-dependent economies of scale.Transportation Research Part E: Logistics and Transportation Review,2026, 207, 104646.(SCI,Q1)
Zhang, W. Y.,Xiao, J. H., Liu, W. S., Sui, Y. F., Li, Y. F., & Zhang, S. Individualized requirements-driven multi-task scheduling in cloud manufacturing using an extended multifactorial evolutionary algorithm.Computers & Industrial Engineering, 2023, 179, 109178.(SCI,Q1)
Zhang, W. Y.,Xiao, J. H., Zhang, S., Lin, J., & Feng, R. J. A utility-aware multi-task scheduling method in cloud manufacturing using an extended NSGA-II embedded with game theory.International Journal of Computer Integrated Manufacturing, 2021, 34(2),175-194.(SCI,Q2)
肖建华,张文雪,潘钰雅,肖久红,牛云云.基于分类垃圾收运时效性的多周期多车舱路径优化研究.中国管理科学, 2025, 33(10): 86-97.(CSSCI)
Xiao, J. H., Lou, X. J., Shi, B. Y.,Xiao, J. H., &Niu, Y. Y. An improved hybrid membrane algorithm based on hormone modulation mechanism for engineering design problems.Expert Systems with Applications, 2023, 227, 120240.(SCI,Q1)
Zhang, S., Chen, Y.,Xiao, J. H., Zhang, W. Y., & Feng, R. J. Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach, attention convolutional neural network, and bidirectional long short-term memory network.Renewable Energy, 2021, 174,688-704.(SCI,Q1)
