[HTML][HTML] A graph reinforcement learning framework for neural adaptive large neighbourhood search

SN Johnn, VA Darvariu, J Handl, J Kalcsics - Computers & Operations …, 2024 - Elsevier
Abstract Adaptive Large Neighbourhood Search (ALNS) is a popular metaheuristic with
renowned efficiency in solving combinatorial optimisation problems. However, despite 18 …

Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning

R Reijnen, Y Zhang, HC Lau, Z Bukhsh - arXiv preprint arXiv:2211.00759, 2022 - arxiv.org
The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable
success in solving complex combinatorial optimization problems (COPs). ALNS selects …

Online control of adaptive large neighborhood search using deep reinforcement learning

R Reijnen, Y Zhang, HC Lau, Z Bukhsh - Proceedings of the …, 2024 - ojs.aaai.org
Abstract The Adaptive Large Neighborhood Search (ALNS) algorithm has shown
considerable success in solving combinatorial optimization problems (COPs). Nonetheless …

Feature-based search space characterisation for data-driven adaptive operator selection

ME Aydin, R Durgut, A Rakib, H Ihshaish - Evolving Systems, 2024 - Springer
Combinatorial optimisation problems are known as unpredictable and challenging due to
their nature and complexity. One way to reduce the unpredictability of such problems is to …

Integrated trucks assignment and scheduling problem with mixed service mode docks: A Q-learning based adaptive large neighborhood search algorithm

Y Li, M Mohammadi, X Zhang, Y Lan… - arXiv preprint arXiv …, 2024 - arxiv.org
Mixed service mode docks enhance efficiency by flexibly handling both loading and
unloading trucks in warehouses. However, existing research often predetermines the …

Learning to Optimise Networked Systems

VA Darvariu - 2023 - discovery.ucl.ac.uk
Many systems based on relations between connected entities find a natural representation
in graphs, which has led to the development of mathematical and statistical tools for …