5 papers · 1 filter
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization
Xia Jiang, Jing Chen, Cong Zhang +5
While large language models (LLMs) have shown strong performance in math and logic reasoning, their ability to handle combinatorial optimization (CO) -- searching high-dimensional…
Aligning LLMs with Graph Neural Solvers for Combinatorial Optimization
Shaodi Feng, Zhuoyi Lin, Yaoxin Wu +4
Recent research has demonstrated the effectiveness of large language models (LLMs) in solving combinatorial optimization problems (COPs) by representing tasks and instances in natu…
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Igor G. Smit, Yaoxin Wu, Pavel Troubil +2
Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has bee…
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization
Xia Jiang, Yaoxin Wu, Yuan Wang +1
To advance capabilities of large language models (LLMs) in solving combinatorial optimization problems (COPs), this paper presents the Language-based Neural COP Solver (LNCS), a no…
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen +6
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the applicat…