All Outputs (3)

Gase: graph attention sampling with edges fusion for solving vehicle routing problems (2024)
Journal Article
Wang, Z., Bai, R., Khan, F., Özcan, E., & Zhang, T. (2024). Gase: graph attention sampling with edges fusion for solving vehicle routing problems. Memetic Computing, 16(3), 337–353. https://doi.org/10.1007/s12293-024-00428-0

Learning-based methods have become increasingly popular for solving vehicle routing problems (VRP) due to their near-optimal performance and fast inference speed. Among them, the combination of deep reinforcement learning and graph representation all... Read More about Gase: graph attention sampling with edges fusion for solving vehicle routing problems.

CUDA-based parallel local search for the set-union knapsack problem (2024)
Journal Article
Sonuç, E., & Özcan, E. (2024). CUDA-based parallel local search for the set-union knapsack problem. Knowledge-Based Systems, 299, Article 112095. https://doi.org/10.1016/j.knosys.2024.112095

The Set-Union Knapsack Problem (SUKP) is a complex combinatorial optimisation problem with applications in resource allocation, portfolio selection, and logistics. This paper presents a parallel local search algorithm for solving SU... Read More about CUDA-based parallel local search for the set-union knapsack problem.

Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts (2024)
Journal Article
Tyasnurita, R., Özcan, E., Drake, J. H., & Asta, S. (2024). Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts. Knowledge-Based Systems, 295, Article 111731. https://doi.org/10.1016/j.knosys.2024.111731

Hyper-heuristics are general purpose search methods for solving computationally difficult problems. A selection hyper-heuristic is composed of two key components: a heuristic selection method and move acceptance criterion. Under an iterative single-p... Read More about Constructing selection hyper-heuristics for open vehicle routing with time delay neural networks using multiple experts.