MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Automation and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学自动化与智能制造学院）；Guangdong Provincial Key Laboratory of Fully Actuated System Control Theory and Technology, Southern University of Science and Technology（广东省全驱动系统控制理论与技术重点实验室，南方科技大学）；Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology（埃因霍温理工大学工业工程与创新科学系）
Yuepeng Zheng, Fu Luo, Zhenkun Wang, Yaoxin Wu, Yu Zhou
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) variants. However, existing Reinforcement Learning (RL)-based multi-task methods can only train light decoder models on small-scale problems, exhibiting limited generalization ability when solving large-scale problems. To overcome this limitation, this work introduces a novel multi-task learning method driven by knowledge distillation (MTL-KD), which enables the efficient training of heavy decoder models with strong generalization ability. The proposed MTL-KD method transfers policy knowledge from multiple distinct RL-based single-task models to a single heavy decoder model, facilitating label-free training and effectively improving the model's generalization ability across diverse tasks. In addition, we introduce a flexible inference strategy termed Random Reordering Re-Construction (R3C), which is specifically adapted for diverse VRP tasks and further boosts the performance of the multi-task model. Experimental results on 6 seen and 10 unseen VRP variants with up to 1000 nodes indicate that our proposed method consistently achieves superior performance on both uniform and real-world benchmarks, demonstrating robust generalization abilities.
Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）；Singapore Institute of Manufacturing Technology, A*STAR（新加坡科技研究局新加坡制造技术研究院）；University of Melbourne（墨尔本大学）
Mitigating Task Interference in Multi-Task Learning via Explicit Task Routing with Non-Learnable Primitives
Beijing Jiaotong University（北京交通大学）；Sun Yat-sen University（中山大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Southern University of Science and Technology（南方科技大学）；Michigan State University（密歇根州立大学）
Towards Omni-generalizable Neural Methods for Vehicle Routing Problems
Nanyang Technological University（南洋理工大学）；Eindhoven University of Technology（埃因霍温理工大学）；Shandong University（山东大学）；Singapore Management University（新加坡管理大学）
Learning to Reduce Search Space for Generalizable Neural Routing Solver
School of Automation and Intelligent Manufacturing（自动化与智能制造学院）；Southern University of Science and Technology（南方科技大学）；School of Mathematics and Statistics（数学与统计学学院）；Xi'an Jiaotong University（西安交通大学）；Department of Computer Science（计算机科学系）；City University of Hong Kong（香港城市大学）
Token-Level Prompt Mixture with Parameter-Free Routing for Federated Domain Generalization
School of Computing and Artificial Intelligence, Shandong University of Finance and Economics（计算机与人工智能学院，山东财经大学）；School of Computer Science and Technology, Shandong Jianzhu University（计算机科学与技术学院，山东建筑大学）；College of Electronics and Information Engineering, Tongji University（电子信息工程学院，同济大学）；School of Information Science and Technology, University of Science and Technology of China（信息科学与技术学院，中国科学技术大学）
Improving Generalization of Neural Vehicle Routing Problem Solvers Through the Lens of Model Architecture
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院、教育部符号计算与知识工程重点实验室）；Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University（南洋理工大学、新加坡国立大学-不列颠哥伦比亚大学老年人积极生活联合卓越研究中心）；WeBank-NTU Joint Research Institute on Fintech, Nanyang Technological University（南洋理工大学微众银行金融科技联合研究院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
Spatial-temporal-demand clustering for solving large-scale vehicle routing problems with time windows
Logistics and Supply Chain Management, School of Management, Technical University of Munich（物流与供应链管理系，管理学院，慕尼黑技术大学）；Munich Data Science Institute (MDSI), Technical University of Munich（慕尼黑数据科学研究所（MDSI），慕尼黑技术大学）
