Fengque Pei
Orcid: 0000-0002-9532-570X
According to our database1,
Fengque Pei
authored at least 16 papers
between 2018 and 2026.
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Bibliography
2026
A study of a matrix manufacturing system scheduling method considering equipment degradation.
Expert Syst. Appl., 2026
2025
Research on AGV path planning in flexible job-shop scheduling problem based on multi-agent deep Q network.
Intell. Serv. Robotics, May, 2025
J. Intell. Manuf., January, 2025
Green flexible job-shop scheduling considering transportation time and machine multi-rotation speeds.
Swarm Evol. Comput., 2025
Multi-objective energy-efficient scheduling of distributed heterogeneous hybrid flow shops via multi-agent double deep Q-Network.
Swarm Evol. Comput., 2025
Distributed heterogeneous flexible job-shop scheduling problem considering automated guided vehicle transportation via improved deep Q network.
Swarm Evol. Comput., 2025
Dynamic integrated process planning and scheduling under multi-resource constraints in workshops with reconfigurable manufacturing cells: a novel hyper-heuristic approach.
Expert Syst. Appl., 2025
Deep reinforcement learning based proximal policy optimization algorithm for dynamic job shop scheduling.
Comput. Oper. Res., 2025
Adv. Eng. Informatics, 2025
2024
Research on optimal path sampling algorithm of manipulator based on potential function.
Int. J. Intell. Robotics Appl., June, 2024
Dynamic scheduling mechanism for intelligent workshop with deep reinforcement learning method based on multi-agent system architecture.
Comput. Ind. Eng., 2024
2023
Comput. Ind. Eng., November, 2023
A multi-agent double Deep-Q-network based on state machine and event stream for flexible job shop scheduling problem.
Adv. Eng. Informatics, October, 2023
2021
Research on intelligent workshop resource scheduling method based on improved NSGA-II algorithm.
Robotics Comput. Integr. Manuf., 2021
2018
Double-layered big data analytics architecture for solar cells series welding machine.
Comput. Ind., 2018
Multi-Level Welding Quality Fault Discovery of an Intelligent Production Line by Using Taguchi Quality Loss Function and Signal-Noise Ratio.
IEEE Access, 2018