Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …

Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …

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 …

This paper introduces RouteFinder, a comprehensive foundation model framework to tackle
different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model …

We introduce RL4CO, an extensive reinforcement learning (RL) for combinatorial
optimization (CO) benchmark. RL4CO employs state-of-the-art software libraries as well as …

Recently, applying neural networks to address combinatorial optimization problems (COPs)
has attracted considerable research attention. The prevailing methods always train deep …

Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing
neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues …

Handcrafting heuristics for solving complex planning tasks (eg, NP-hard combinatorial
optimization (CO) problems) is a common practice but requires extensive domain …

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

Abstract Information extraction from Electronic Health Records (EHRs) is a crucial task in
healthcare, and the lack of resources and language specificity pose significant challenges …