5 papers
Efficient Decoder Scaling Strategy for Neural Routing Solvers
Qing Luo, Fu Luo, Ke Li +1
Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent s…
Improving Generalization of Neural Combinatorial Optimization for Vehicle Routing Problems via Test-Time Projection Learning
Yuanyao Chen, Rongsheng Chen, Fu Luo +1
Neural Combinatorial Optimization (NCO) has emerged as a promising learning-based paradigm for addressing Vehicle Routing Problems (VRPs) by minimizing the need for extensive manua…
MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
Yuepeng Zheng, Fu Luo, Zhenkun Wang +2
Multi-Task Learning (MTL) in Neural Combinatorial Optimization (NCO) is a promising approach to train a unified model capable of solving multiple Vehicle Routing Problem (VRP) vari…
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees
Fu Luo, Yaoxin Wu, Zhi Zheng +1
Recent neural combinatorial optimization (NCO) methods have shown promising problem-solving ability without requiring domain-specific expertise. Most existing NCO methods use train…
Learning to Insert for Constructive Neural Vehicle Routing Solver
Fu Luo, Xi Lin, Mengyuan Zhong +4
Neural Combinatorial Optimisation (NCO) is a promising learning-based approach for solving Vehicle Routing Problems (VRPs) without extensive manual design. While existing construct…