Multi-objective energy-efficient hybrid flow shop scheduling using Q-learning and GVNS driven NSGA-II
P Li, Q Xue, Z Zhang, J Chen, D Zhou - Computers & Operations Research, 2023 - Elsevier
The urgent mission for carbon peak and carbon neutrality is demanding greater industrial
sustainability. Energy-efficient hybrid flow shop scheduling problem (EEHFSP) has been …
sustainability. Energy-efficient hybrid flow shop scheduling problem (EEHFSP) has been …
Energy-aware remanufacturing process planning and scheduling problem using reinforcement learning-based particle swarm optimization algorithm
J Wang, H Zheng, S Zhao, Q Zhang - Journal of Cleaner Production, 2024 - Elsevier
Solving remanufacturing process planning and scheduling problem collaboratively and
leveraging the complementary attributes of process planning and shop scheduling to attain …
leveraging the complementary attributes of process planning and shop scheduling to attain …
[HTML][HTML] A graph reinforcement learning framework for neural adaptive large neighbourhood search
Abstract Adaptive Large Neighbourhood Search (ALNS) is a popular metaheuristic with
renowned efficiency in solving combinatorial optimisation problems. However, despite 18 …
renowned efficiency in solving combinatorial optimisation problems. However, despite 18 …
Transfer learning for operator selection: A reinforcement learning approach
In the past two decades, metaheuristic optimisation algorithms (MOAs) have been
increasingly popular, particularly in logistic, science, and engineering problems. The …
increasingly popular, particularly in logistic, science, and engineering problems. The …
Local optima correlation assisted adaptive operator selection
For solving combinatorial optimisation problems with metaheuristics, different search
operators are applied for sampling new solutions in the neighbourhood of a given solution. It …
operators are applied for sampling new solutions in the neighbourhood of a given solution. It …
[HTML][HTML] CUDA-based parallel local search for the set-union knapsack problem
Abstract The Set-Union Knapsack Problem (SUKP) is a complex combinatorial optimisation
problem with applications in resource allocation, portfolio selection, and logistics. This paper …
problem with applications in resource allocation, portfolio selection, and logistics. This paper …
Adaptive operator selection utilising generalised experience
Optimisation problems, particularly combinatorial optimisation problems, are difficult to solve
due to their complexity and hardness. Such problems have been successfully solved by …
due to their complexity and hardness. Such problems have been successfully solved by …
[HTML][HTML] Multi-Objective Path Planning for Unmanned Sweepers Considering Traffic Signals: A Reinforcement Learning-Enhanced NSGA-II Approach
With the widespread popularization of unmanned sweepers, path planning has been
recognized as a key component affecting their total work efficiency. Conventional path …
recognized as a key component affecting their total work efficiency. Conventional path …
Emergency Scheduling of Aerial Vehicles via Graph Neural Neighborhood Search
The thriving advances in autonomous vehicles and aviation have enabled the efficient
implementation of aerial last-mile delivery services to meet the pressing demand for urgent …
implementation of aerial last-mile delivery services to meet the pressing demand for urgent …
Multiple search operators selection by adaptive probability allocation for fast convergent multitask optimization
Z Wang, L Wang, Q Jiang, X Duan, Z Wang… - The Journal of …, 2024 - Springer
Evolutionary multitask optimization (EMTO) has developed fast recently, and many
algorithms have emerged that solve several different problems simultaneously through …
algorithms have emerged that solve several different problems simultaneously through …