Search Results for author:
Found 9 papers, 6 papers with code
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
Despite the recent progress made in learning to solve individual VRP variants, there is a lack of a unified approach that can effectively tackle a wide range of tasks, which is crucial for real-world impact.
PolyNet: Learning Diverse Solution Strategies for Neural Combinatorial Optimization
Reinforcement learning-based methods for constructing solutions to combinatorial optimization problems are rapidly approaching the performance of human-designed algorithms.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
Simulation-guided Beam Search for Neural Combinatorial Optimization
Neural approaches for combinatorial optimization (CO) equip a learning mechanism to discover powerful heuristics for solving complex real-world problems.
The First AI4TSP Competition: Learning to Solve Stochastic Routing Problems
Overall, by organizing this competition we have introduced routing problems as an interesting problem setting for AI researchers.
Efficient Active Search for Combinatorial Optimization Problems
While active search is simple to implement, it is not competitive with state-of-the-art methods because adjusting all model weights for each test instance is very time and memory intensive.
Learning a Latent Search Space for Routing Problems using Variational Autoencoders
Methods for automatically learning to solve routing problems are rapidly improving in performance.
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem
Furthermore, we show for the CVRP and the SDVRP that our approach surpasses the performance of existing machine learning approaches and comes close to the performance of state-of-the-art optimization approaches.
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem
The container pre-marshalling problem (CPMP) is concerned with the re-ordering of containers in container terminals during off-peak times so that containers can be quickly retrieved when the port is busy.
