13 papers
Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
Wei Liu, Yaoxin Wu, Yingqian Zhang +2
Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs). Nevertheless, the robustness of these learning…
Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
Xia Jiang, Yaoxin Wu, Yew-Soon Ong +1
Neural policies have shown promise in solving vehicle routing problems due to their reduced reliance on handcrafted heuristics. However, current training paradigms suffer from a fu…
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…
Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks
Minshuo Li, Yaoxin Wu, Pavel Troubil +2
Complex real-world optimization problems often involve both discrete decisions and nonlinear relationships between variables. Many such problems can be modeled as polynomial-object…
End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
Abdo Abouelrous, Laurens Bliek, Yaoxin Wu +1
In this work, we consider learning-based applications in routing to solve a Vehicle Routing variant characterized by stochasticity and multiple objectives. Such problems are repres…