Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …

Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …

Recently, deep reinforcement learning (DRL) models have shown promising results in
solving NP-hard Combinatorial Optimization (CO) problems. However, most DRL solvers …

The recent end-to-end neural solvers have shown promise for small-scale routing problems
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …

Learning-based algorithms to solve TSP are getting popular in recent years, but most
existing works cannot solve very large-scale TSP instances within a limited time. To solve …

Applying machine learning to combinatorial optimization problems has the potential to
improve both efficiency and accuracy. However, existing learning-based solvers often …

Game theory provides a mathematical way to study the interaction between multiple
decision makers. However, classical game-theoretic analysis is limited in scalability due to …

Vehicle Routing Problems (VRPs) are significant Combinatorial Optimization (CO) problems
holding substantial practical importance. Recently, Neural Combinatorial Optimization …

Existing methods provide varying algorithms for different types of Boolean satisfiability
problems (SAT), lacking a general solution framework. Accordingly, this study proposes a …

Spanning tree problems with specialized constraints can be difficult to solve in real-world
scenarios, often requiring intricate algorithmic design and exponential time. Recently, there …