Outputs (6)

Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes (2024)
Journal Article
Barrera, M. C., Leech, D., Josifovic, A., Tolouei, A., Alford, G., Wallace, M. J., Bennett, N., Wildman, R., Irvine, D. J., Croft, A., Özcan, E., Florence, A. J., Johnston, B., Robertson, J., & Brown, C. J. (2025). Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes. Chemical Engineering Research and Design, 213, 126-136. https://doi.org/10.1016/j.cherd.2024.11.040

This article reports the development and utilisation of an adaptive design workflow methodology for use as a platform technology for the printing, testing, and optimisation of biopharmaceutical processing reactors. This design strategy was developed... Read More about Optimisation of additively manufactured coiled flow inverters for continuous viral inactivation processes.

FiDRL: Flexible Invocation-Based Deep Reinforcement Learning for DVFS Scheduling in Embedded Systems (2024)
Journal Article
Li, J., Jiang, W., He, Y., Yang, Q., Gao, A., Ha, Y., Özcan, E., Bai, R., Cui, T., & Yu, H. (2024). FiDRL: Flexible Invocation-Based Deep Reinforcement Learning for DVFS Scheduling in Embedded Systems. IEEE Transactions on Computers, 74(1), 71 - 85. https://doi.org/10.1109/TC.2024.3465933

Deep Reinforcement Learning (DRL)-based Dynamic Voltage Frequency Scaling (DVFS) has shown great promise for energy conservation in embedded systems. While many works were devoted to validating its efficacy or improving its performance, few discuss t... Read More about FiDRL: Flexible Invocation-Based Deep Reinforcement Learning for DVFS Scheduling in Embedded Systems.

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.

Efficient Multi-Objective Simulation Metamodeling for Researchers (2024)
Journal Article
Ho, K. J., Özcan, E., & Siebers, P.-O. (2024). Efficient Multi-Objective Simulation Metamodeling for Researchers. Algorithms, 17(1), Article 41. https://doi.org/10.3390/a17010041

Solving multiple objective optimization problems can be computationally intensive even when experiments can be performed with the help of a simulation model. There are many methodologies that can achieve good tradeoffs between solution quality and re... Read More about Efficient Multi-Objective Simulation Metamodeling for Researchers.