个人简历

黄黛麟，博士。女，1997年生，2019年获南京邮电大学工学学士学位，2022年获兰州理工大学工学硕士学位，2025年获兰州理工大学工学博士学位。近五年在Computers & Industrial Engineering、Applied Soft Computing、Measurement以及Journal of Control and Decision等期刊发表过13篇论文。参与国家重点研发计划、国家自然科学基金面上项目、甘肃省重点研发计划以及华为技术公司主导的算法测试项目等。

研究方向

强化学习的工业应用、智慧交通、计算机视觉等。

主要承担的教学课程

硕士生课程：智能决策理论与应用

本科生课程：C语言程序设计

论文及专利

Huang D, Zhao H, Tian W, et al. A deep reinforcement learning method based on a multiexpert graph neural network for flexible job shop scheduling[J]. Computers & Industrial Engineering, 2025, 200: 110768.（JCR Q1，中科院二区Top期刊，IF=6.5）

Huang D, Zhao H, Cao J, et al. Optimizing the flexible job shop scheduling problem via deep reinforcement learning with mean multichannel graph attention[J]. Applied Soft Computing, 2025: 113128.（JCR Q1，中科院二区Top期刊，IF=6.6）

Cao J, Ma J, Huang D, et al. Method to enhance deep learning fault diagnosis by generating adversarial samples[J]. Applied Soft Computing, 2022, 116: 108385. （JCR Q1，中科院二区Top期刊，IF=6.6）

Cao J, Ma J, Huang D, et al. Finding the optimal multilayer network structure through reinforcement learning in fault diagnosis[J]. Measurement, 2022, 188: 110377. （JCR Q1，中科院二区，IF=5.6）

Zhao H, Li W, Huang D, et al. M-GAN: multiattribute learning and multimodal feature fusion-based generative adversarial network for text-to-image synthesis[J]. The Visual Computer, 2024: 1-19. （中科院三区，IF=2.9）

Huang D, Zhao H, Zhang L, et al. Learning to dispatch for flexible job shop scheduling based on deep reinforcement learning via graph gated channel transformation[J]. IEEE Access, 2024, 12: 50935-50948.（中科院四区，IF=3.6）

Zhao H, Huang D, Tian W, et al. Research on Flexible Job Shop Scheduling Based on Deep Reinforcement Learning with Transformer-Graph Neural Network[J] Journal of Control and Decision, 2025. （中科院四区，IF=1.8）

Cao J, Huang D, Hou L, et al. A multi process value-based reinforcement learning environment framework for adaptive traffic signal control[J]. Journal of Control and Decision, 2023, 10(2): 229-236.（中科院四区，IF=1.8）

Hou L, Huang D, Cao J, et al. Multi-agent deep reinforcement learning with traffic flow for traffic signal control[J]. Journal of Control and Decision, 2025, 12(1): 81-92.（中科院四区，IF=1.8）

联系方式

Email：03468@huznu.edu.cn