Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
Rl4co: an extensive reinforcement learning for combinatorial optimization benchmark
We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However,
most neural solvers are only structured and trained independently on a specific problem …
most neural solvers are only structured and trained independently on a specific problem …
Learning encodings for constructive neural combinatorial optimization needs to regret
Abstract Deep-reinforcement-learning (DRL) based neural combinatorial optimization (NCO)
methods have demonstrated efficiency without relying on the guidance of optimal solutions …
methods have demonstrated efficiency without relying on the guidance of optimal solutions …
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 …
optimization problems are rapidly approaching the performance of human-designed …
Moco: A Learnable Meta Optimizer for Combinatorial Optimization
T Dernedde, D Thyssens, S Dittrich… - arXiv preprint arXiv …, 2024 - arxiv.org
Relevant combinatorial optimization problems (COPs) are often NP-hard. While they have
been tackled mainly via handcrafted heuristics in the past, advances in neural networks …
been tackled mainly via handcrafted heuristics in the past, advances in neural networks …
Self-Improvement for Neural Combinatorial Optimization: Sample without Replacement, but Improvement
Current methods for end-to-end constructive neural combinatorial optimization usually train
a policy using behavior cloning from expert solutions or policy gradient methods from …
a policy using behavior cloning from expert solutions or policy gradient methods from …
Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and More
Combinatorial optimization (CO) is naturally discrete, making machine learning based on
differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic …
differentiable optimization inapplicable. Karalias & Loukas (2020) adapted the probabilistic …
A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks
Graph neural networks (GNNs) have emerged as a powerful tool for solving combinatorial
optimization problems (COPs), exhibiting state-of-the-art performance in both graph …
optimization problems (COPs), exhibiting state-of-the-art performance in both graph …
Collaboration! Towards Robust Neural Methods for Routing Problems
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing
neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues …
neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues …