Ming Huang's Homepage
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Academic Publications

Published papers

2026
Integrated process planning, reconfiguration, and scheduling optimization for smart manufacturing systems with reconfigurable machine tools based on deep reinforcement learning
Huang, M., Huang, S., Mo, G., Moghaddam, S. K., Zhao, Z., Dahane, M., Wang, G., Yan, Y.
Engineering 2026IF: 12.2 ; JCR Q1
To address the growing demand for mass personalized manufacturing under Industry 4.0, this work studies integrated process planning, reconfiguration, and scheduling for reconfigurable smart manufacturing systems. A DRL-based optimization framework is developed to handle the coupled decision-making problem, while emphasizing the emerging role of large language models (LLMs) as a promising paradigm for enhancing industrial decision intelligence. Experimental results demonstrate the effectiveness of the proposed approach, and further highlight the potential of LLMs for future integrated manufacturing optimization.
Digital twin-driven multi-agent collaborative online optimization of production regulation for smart reconfigurable manufacturing systems with human-robot collaboration
Huang, M., Huang, S., Gao, L., Dong, W., Zhang, Y., Gu, X., Gao, Z.
Robotics and Computer-Integrated Manufacturing 2026IF: 12.3 ; JCR Q1; TOP
A digital twin-driven multi-agent online optimization framework is proposed for smart reconfigurable manufacturing systems with human–robot collaboration (SRMS-HRC). Multi-objective deep reinforcement learning is employed to enable real-time production regulation under dynamic and uncertain conditions, replacing conventional offline optimization paradigms.
2023-2025
Fuzzy superposition operation and knowledge-driven coevolutionary algorithm for integrated production scheduling and vehicle routing problem with soft time windows and fuzzy travel times
Huang, M., Huang, S., Du, B., Guo, J., Li, Y.
IEEE Transactions on Fuzzy Systems 2025IF: 11.9 ; JCR Q1; TOP
This work studies an integrated production scheduling and vehicle routing problem with fuzzy travel times and soft time windows. A key contribution is the proposed fuzzy superposition operation for modeling fuzzy weighted early–tardy penalties, further generalized into a new fuzzy aggregation law. A biobjective optimization model is formulated and validated using the CPLEX solver with the ε-constraint method. In addition, a knowledge-driven coevolutionary algorithm (KDCEA) is developed for large-scale instances, and the implementation is released as open-source code. Experimental results demonstrate the effectiveness and superiority of the proposed approach.
A hybrid collaborative framework for integrated production scheduling and vehicle routing problem with batch manufacturing and soft time windows
Huang, M., Du, B., Guo, J.
Computers & Operations Research 2023IF: 4.6 ; JCR Q1
A hybrid collaborative framework is proposed for integrated production scheduling and vehicle routing with batch manufacturing and multitrip heterogeneous vehicle delivery. The framework combines an exact coordination mechanism for production–routing synchronization with an evolutionary optimization method enhanced by adaptive large neighborhood search, enabling high-quality integrated decision-making.
Dynamic scheduling optimization of island assembly lines under uncertain disturbances by multi-objective deep reinforcement learning.
Huang, M., Huang, S., Chen, J, Dong, W., Wang, B., Ruan, B., Gao, Y., Wang, G., Yan, Y.
Journal of Mechanical Engineering(in Chinese) 2025EI index
This paper investigates an island assembly system (IAS) for automotive manufacturing under multi-source uncertainties, including dynamic disturbances such as urgent order insertions. A bi-objective optimization model is developed to minimize makespan and schedule change index. To address the resulting dynamic scheduling problem, a multi-objective deep reinforcement learning approach is proposed, enabling adaptive selection of dispatching rules under varying system states. Experimental results on multiple-scale instances demonstrate that the proposed method significantly outperforms heuristic rules, random strategies, and conventional DRL-based methods in terms of solution quality and robustness.
Integrated production and distribution scheduling optimization of considering soft time windows and fuzzy travel time
Huang, M., Du, B., Guo, J., Li, Y.
Control Theory and Applications(in Chinese) 2024EI index
An NSGA-II enhanced with adaptive variable neighborhood search (NSGA-II-AVNS) is developed for an integrated scheduling and distribution problem with fuzzy travel times and soft time windows. A key feature is a three-stage decoding rule that decomposes the decision process into batch formation, routing construction, and coordinated scheduling. The adaptive neighborhood selection and reset mechanism significantly improves search diversity and solution quality.

Cooperative Publications

2025-2026
  1. Huang, S*(Supervisor), Huang, M., Zhang, Y., Peng, Z., Zhang, Z., Xu, Z., Xing, H., Zhang, X., Wang, G., Yan, Y. (2026). A review of robotic smart manufacturing for aviation industry: Development status, key technologies and typical scenarios. Aeronautical Manufacturing Technology .69(5) 25010147. (EI, in Chinese，Cover Story)
  2. Huang, S(Supervisor), Huang, M., Wang, G., Yan, Y. (2026). Intelligent Clustering Method of Part Family Formation for D-RMS. In Design and Operation of Smart Reconfigurable Manufacturing Systems in Industry 4.0/5.0. Springer Cham . https://doi.org/10.1007/978-3-032-00284-6_2 (Chapter)
  3. Huang, S(Supervisor), Huang, M., Zhu, Q., Wang, G., Yan, Y. (2025). Operation tasks assignment optimization of human–robot collaboration manufacturing system based on NSGA-II. In Human-centric smart manufacturing towards industry 5.0. Springer Cham. https://doi.org/10.1007/978-3-031-82170-7_9. (Chapter)
  4. Sun, Y, Huang, M., Liu, H., Huang, J., Tao, J., Xu, B., Qiao, D., Huang, S.* (2025). Integrated optimization of fiber optic gyroscope assembly shop layout and production scheduling based on lean logistics. Industrial Engineering Journal . 28(04): 24-33. (in Chinese)
  5. Zhu, J., Huang, S*, Huang, M., Wang, G., Yan, Y. (2025). Production scheduling optimization paradigm based on DeepSeek R1: A case of island assembly. Computer Integrated Manufacturing Systems . 1-18. (EI, accepted, in Chinese)
  6. Huang, S.*, Ma, N., Peng, Z., Huang, M., Zhang, Z., Zhao, Z., Jin X., Wang, G., Yan, Y. (2025) New benchmark dataset driven reconfiguration path optimization for smart RMT using NSGA-III, Journal of Advanced Manufacturing Science and Technology . 2026005-2026000. https://doi.org/10.51393/j.jamst.2026005. (Accepted)

Master’s Thesis

  1. Huang, M., 2023. Research on integrated production and distribution scheduling for fast moving consumer goods manufacturing enterprise. Wuhan University of Technology . (Master’s Thesis) HTML;

Working papers

  1. Ming Huang, Sihan Huang, Bing Ruan, Wei Dong, Jianpeng Chen, Baicun Wang, Baigang Du, Guoxin Wang, Yan Yan, Lihui Wang. A normalized Tchebycheff aggregation based multi-objective deep reinforcement learning for dynamic flexible island assembly system scheduling.
    (Paper) HTML ; (NT-D3QN) python code ; (DFIASSP-Dataset) .zip ; (DFIASSP-Case) .zip; (AMPL) *.mod

Conference papers

  • None