:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Abouelrous, Abdo, Bliek, Laurens, Gabor, Adriana F., Wu, Yaoxin, Zhang, Yingqian
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2504.02383
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application
by: Abouelrous, Abdo, et al.
Published: (2025)

End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
by: Abouelrous, Abdo, et al.
Published: (2025)

Enhancing the Cross-Size Generalization for Solving Vehicle Routing Problems via Continual Learning
by: Li, Jingwen, et al.
Published: (2025)

Cross-Problem Learning for Solving Vehicle Routing Problems
by: Lin, Zhuoyi, et al.
Published: (2024)

Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
by: Jiang, Xia, et al.
Published: (2026)

Partial Column Generation with Graph Neural Networks for Team Formation and Routing
by: Dall'Olio, Giacomo, et al.
Published: (2025)

Learning to Handle Complex Constraints for Vehicle Routing Problems
by: Bi, Jieyi, et al.
Published: (2024)

EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems
by: Meng, Dian, et al.
Published: (2025)

Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees
by: Luo, Fu, et al.
Published: (2025)

Enhancing Cross-Problem Vehicle Routing via Federated Learning
by: Meng, Xiangchi, et al.
Published: (2026)

Automated Reinforcement Learning: An Overview
by: Afshar, Reza Refaei, et al.
Published: (2022)

Adversarial Generative Flow Network for Solving Vehicle Routing Problems
by: Zhang, Ni, et al.
Published: (2025)

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
by: Liu, Wei, et al.
Published: (2026)

Deep Reinforcement Learning for Solving the Fleet Size and Mix Vehicle Routing Problem
by: Wan, Pengfu, et al.
Published: (2025)

Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
by: Reijnen, Robbert, et al.
Published: (2025)

MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
by: Zheng, Yuepeng, et al.
Published: (2025)

Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
by: Smit, Igor G., et al.
Published: (2024)

Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
by: Zhang, Cong, et al.
Published: (2024)

Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks
by: Li, Minshuo, et al.
Published: (2026)

Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet
by: Deineko, Elija, et al.
Published: (2024)

EXPObench: Benchmarking Surrogate-based Optimisation Algorithms on Expensive Black-box Functions
by: Bliek, Laurens, et al.
Published: (2021)

Collaboration! Towards Robust Neural Methods for Routing Problems
by: Zhou, Jianan, et al.
Published: (2024)

Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
by: Reijnen, Robbert, et al.
Published: (2023)

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
by: Zhou, Jianan, et al.
Published: (2024)

Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
by: Huang, Shihong, et al.
Published: (2026)

Reinforcement Learning for Multi-Truck Vehicle Routing Problems
by: Levin, Joshua, et al.
Published: (2022)

Learning to Segment for Vehicle Routing Problems
by: Ouyang, Wenbin, et al.
Published: (2025)

SED2AM: Solving Multi-Trip Time-Dependent Vehicle Routing Problem using Deep Reinforcement Learning
by: Mozhdehi, Arash, et al.
Published: (2025)

GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems
by: Wang, Zhenwei, et al.
Published: (2024)

Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning
by: Li, Qi, et al.
Published: (2025)

Graph Neural Networks for Job Shop Scheduling Problems: A Survey
by: Smit, Igor G., et al.
Published: (2024)

Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
by: Levin, Joshua, et al.
Published: (2024)

A Unified Deep Reinforcement Learning Approach for Close Enough Traveling Salesman Problem
by: Fan, Mingfeng, et al.
Published: (2025)

Mamba Meets Scheduling: Learning to Solve Flexible Job Shop Scheduling with Efficient Sequence Modeling
by: Cao, Zhi, et al.
Published: (2026)

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Data
by: van Remmerden, Jesse, et al.
Published: (2025)

Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
by: Bi, Jieyi, et al.
Published: (2026)

Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Experts
by: Miao, Changhao, et al.
Published: (2026)

An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems
by: Miao, Changhao, et al.
Published: (2025)

Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints
by: Mozhdehi, Arash, et al.
Published: (2024)

Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem
by: Schoepf, Stefan, et al.
Published: (2023)