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[HTML][HTML] Graph neural networks for job shop scheduling problems: A survey
Job shop scheduling problems (JSSPs) represent a critical and challenging class of
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …
combinatorial optimization problems. Recent years have witnessed a rapid increase in the …
Ant colony sampling with gflownets for combinatorial optimization
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic
method that hierarchically combines amortized inference and parallel stochastic search. Our …
method that hierarchically combines amortized inference and parallel stochastic search. Our …
Routefinder: Towards foundation models for vehicle routing problems
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 …
different Vehicle Routing Problem (VRP) variants. Our core idea is that a foundation model …
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 …
UDC: A unified neural divide-and-conquer framework for large-scale combinatorial optimization problems
Single-stage neural combinatorial optimization solvers have achieved near-optimal results
on various small-scale combinatorial optimization (CO) problems without requiring expert …
on various small-scale combinatorial optimization (CO) problems without requiring expert …
Learning to handle complex constraints for vehicle routing problems
Abstract Vehicle Routing Problems (VRPs) can model many real-world scenarios and often
involve complex constraints. While recent neural methods excel in constructing solutions …
involve complex constraints. While recent neural methods excel in constructing solutions …
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 …
Unco: Towards unifying neural combinatorial optimization through large language model
Recently, applying neural networks to address combinatorial optimization problems (COPs)
has attracted considerable research attention. The prevailing methods always train deep …
has attracted considerable research attention. The prevailing methods always train deep …
GOAL: A Generalist Combinatorial Optimization Agent Learning
Machine Learning-based heuristics have recently shown impressive performance in solving
a variety of hard combinatorial optimization problems (COPs). However they generally rely …
a variety of hard combinatorial optimization problems (COPs). However they generally rely …
CaDA: Cross-Problem Routing Solver with Constraint-Aware Dual-Attention
Vehicle Routing Problems (VRPs) are significant Combinatorial Optimization (CO) problems
holding substantial practical importance. Recently, Neural Combinatorial Optimization …
holding substantial practical importance. Recently, Neural Combinatorial Optimization …