< Logistics Management
Intelligent Transportation and Smart Shipping, Scheduling Optimization, Deep Reinforcement Learning, Edge Intelligence, Low-Altitude Technologies.
Dr. Qiangqiang Jiang is a Lecturer at the Faculty of Maritime and Transportation, Ningbo University. He received his Ph.D. in Aerospace Science and Technology from Harbin Institute of Technology (2022~2025), with a joint doctoral training period at The Hong Kong Polytechnic University (2023~2025). He obtained his M.Eng. in Software Engineering from Zhejiang University (2018~2020) and his B.Eng. in Software Engineering from Jiangsu University of Science and Technology (2014~2018).
His research focuses on applying AI techniques (e.g., computational intelligence, deep reinforcement learning, large language models, etc.) to scheduling, planning, and decision-making problems in UAV-assisted maritime and port transportation scenarios. He has published over 10 papers in reputable international journals and conferences, such as IEEE Transactions on Aerospace and Electronic Systems, IEEE Transactions on Geoscience and Remote Sensing, IEEE Internet of Things Journal, and International Journal of Production Research, etc. He has participated in multiple national and provincial research projects, including the National Key Research and Development Program of China, the Guangdong Provincial Natural Science Foundation, etc.
To be specified.
Papers
[1] Q. Jiang, L. Zheng, H. Liu, Y. Zhou, Q. Kong*, Y. Zhang, B. Chen*. Efficient On-Orbit Remote Sensing Imagery Processing via Satellite Edge Computing Resource Scheduling Optimization, IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 1-19.
[2] Q. Jiang, P. Han*, X. Xin, K. Chen. Deep Reinforcement Learning and Edge Computing for Multisatellite On-Orbit Task Scheduling, IEEE Transactions on Aerospace and Electronic Systems, 2025, 61(5): 14143-14159.
[3] Q. Jiang, H. Wang, Y. Zhang, Q, Kong, B. Chen*. On-Orbit Remote Sensing Image Processing Complex Task Scheduling Model Based on Heterogeneous Multiprocessor, IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 1-18.
[4] Q. Jiang, X. Xin, T. Zhang, K. Chen*. Energy-Efficient Task Scheduling and Resource Allocation in Edge Heterogeneous Computing Systems Using Multi-Objective Optimization, IEEE Internet of Things Journal, 2025, 12(17): 36747-36764.
[5] Q. Jiang, X. Xin, L. Yao, B. Chen*. METSM: Multiobjective energy-efficient task scheduling model and method for an edge heterogeneous multiprocessor system, Future Generation Computer Systems, 2024, 152: 207-223.
[6] X. Xin, Q. Jiang*, S. Li, S. Gong, K. Chen. Energy-efficient scheduling for a permutation flow shop with variable transportation time using an improved discrete whale swarm optimization, Journal of Cleaner Production, 2021, 293: 126121.
[7] X. Xin, Q. Jiang*, C. Li, S. Li, K. Chen. Permutation flow shop energy-efficient scheduling with a position-based learning effect, International Journal of Production Research, 2021, 61(2): 382–409.
[8] Q. Jiang, Y. Guo*, Z. Yang, Z. Wang, X. Zhou. Improving the performance of whale optimization algorithm through OpenCL-based FPGA accelerator, Complexity, 2020, 1: 8810759.
[9] Q. Jiang, Y. Guo, Z. Yang*, X. Zhou. A parallel whale optimization algorithm and its implementation on FPGA, 2020 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2020.
[10] L. Zheng, Q. Jiang, Y. Zhang, B. Chen*. Deep Reinforcement Learning for Joint Observation and On-Orbit Computation Scheduling in Agile Satellite Constellations. Aerospace. 2025; 12(10): 914.
[11] H. Zhao, Y. Zhang, Q. Jiang, X. Wei, S. Li, B. Chen*. Software-Defined Satellite Observation: A Fast Method Based on Virtual Resource Pools, Remote Sensing. 2023, 15(22): 5388.
Patents
[1] Heterogeneous Task Scheduling Method and Device for Rotating Sweep Ultra‑Wide‑Swath Satellites, 2024 (Granted, Patent No. ZL202111599786.X).
[2] Energy‑Efficient Heterogeneous Multi‑Core Scheduling Method and Device for Maritime Unmanned Equipment, 2026 (Granted, Patent No. ZL202210879082.6).
[3] On‑Orbit Heterogeneous Computing Method for Rotating Sweep Ultra‑Wide‑Swath Satellites, 2021 (Substantive Examination, Patent No. CN202111618999.2).
[4] Deep Reinforcement Learning Task Scheduling Method and Device for Maritime Unmanned Equipment, 2022 (Substantive Examination, Patent No. CN202210880692.8).
