| 2026 | AAAI | Scale-Net: A Hierarchical U-Net Framework for Cross-Scale Generalization in Multi-Task Vehicle Routing. | Suyu Liu, Zhiguang Cao, Nan Yin, Yew-Soon Ong |
| 2026 | AAAI | Instance Generation for Meta-Black-Box Optimization Through Latent Space Reverse Engineering. | Chen Wang, Yue-Jiao Gong, Zhiguang Cao, Zeyuan Ma |
| 2026 | AAAI | Bridging Synthetic and Real Routing Problems via LLM-Guided Instance Generation and Progressive Adaptation. | Jianghan Zhu, Yaoxin Wu, Zhuoyi Lin, Zhengyuan Zhang, Haiyan Yin, Zhiguang Cao, Senthilnath Jayavelu, Xiaoli Li |
| 2025 | AAAI | ConfigX: Modular Configuration for Evolutionary Algorithms via Multitask Reinforcement Learning. | Hongshu Guo, Zeyuan Ma, Jiacheng Chen, Yining Ma, Zhiguang Cao, Xinglin Zhang, Yue-Jiao Gong |
| 2025 | AAAI | DualOpt: A Dual Divide-and-Optimize Algorithm for the Large-scale Traveling Salesman Problem. | Shipei Zhou, Yuandong Ding, Chi Zhang, Zhiguang Cao, Yan Jin |
| 2025 | GECCO | Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study. | Zeyuan Ma, Zhiyang Huang, Jiacheng Chen, Zhiguang Cao, Yue-Jiao Gong |
| 2025 | GECCO | MILPBench: A Large-scale Benchmark Test Suite for Mixed Integer Linear Programming Problems. | Huigen Ye, Yaoyang Cheng, Hua Xu, Zhiguang Cao, Hanzhang Qin |
| 2025 | ICLR | Rethinking Neural Multi-Objective Combinatorial Optimization via Neat Weight Embedding. | Jinbiao Chen, Zhiguang Cao, Jiahai Wang, Yaoxin Wu, Hanzhang Qin, Zizhen Zhang, Yue-Jiao Gong |
| 2025 | ICLR | Neural Multi-Objective Combinatorial Optimization via Graph-Image Multimodal Fusion. | Jinbiao Chen, Jiahai Wang, Zhiguang Cao, Yaoxin Wu |
| 2025 | ICLR | Rethinking Light Decoder-based Solvers for Vehicle Routing Problems. | Ziwei Huang, Jianan Zhou, Zhiguang Cao, Yixin Xu |
| 2025 | ICLR | Graph Assisted Offline-Online Deep Reinforcement Learning for Dynamic Workflow Scheduling. | Yifan Yang, Gang Chen, Hui Ma, Cong Zhang, Zhiguang Cao, Mengjie Zhang |
| 2025 | ICLR | Adversarial Generative Flow Network for Solving Vehicle Routing Problems. | Ni Zhang, Jingfeng Yang, Zhiguang Cao, Xu Chi |
| 2025 | ICML | SHIELD: Multi-task Multi-distribution Vehicle Routing Solver with Sparsity and Hierarchy. | Yong Liang Goh, Zhiguang Cao, Yining Ma, Jianan Zhou, Mohammed Haroon Dupty, Wee Sun Lee |
| 2025 | ICML | A Mixed-Curvature based Pre-training Paradigm for Multi-Task Vehicle Routing Solver. | Suyu Liu, Zhiguang Cao, Shanshan Feng, Yew-Soon Ong |
| 2025 | ICML | Meta-Black-Box-Optimization through Offline Q-function Learning. | Zeyuan Ma, Zhiguang Cao, Zhou Jiang, Hongshu Guo, Yue-Jiao Gong |
| 2025 | IJCAI | EFormer: An Effective Edge-based Transformer for Vehicle Routing Problems. | Dian Meng, Zhiguang Cao, Yaoxin Wu, Yaqing Hou, Hongwei Ge, Qiang Zhang |
| 2025 | IJCAI | Preference-based Deep Reinforcement Learning for Historical Route Estimation. | Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, Qiang Zhang |
| 2025 | IJCAI | DGL: Dynamic Global-Local Information Aggregation for Scalable VRP Generalization with Self-Improvement Learning. | Yubin Xiao, Yuesong Wu, Rui Cao, Di Wang, Zhiguang Cao, Xuan Wu, Peng Zhao, Yuanshu Li, You Zhou, Yuan Jiang |
| 2025 | IJCAI | Coupling Category Alignment for Graph Domain Adaptation. | Nan Yin, Xiao Teng, Zhiguang Cao, Mengzhu Wang |
| 2025 | KDD | Enhancing Generalization in Large-Scale HCVRP: A Rank-Augmented Neural Solver. | Qidong Liu, Jiurui Lian, Chaoyue Liu, Zhiguang Cao |
| 2025 | KDD | RL4CO: An Extensive Reinforcement Learning for Combinatorial Optimization Benchmark. | Federico Berto, Chuanbo Hua, Junyoung Park, Laurin Luttmann, Yining Ma, Fanchen Bu, Jiarui Wang, Haoran Ye, Minsu Kim, Sanghyeok Choi, Nayeli Gast Zepeda, Andr Hottung, Jianan Zhou, Jieyi Bi, Yu Hu, Fei Liu, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Davide Angioni, Wouter Kool, Zhiguang Cao, Qingfu Zhang, Joungho Kim, Jie Zhang, Kijung Shin, Cathy Wu, Sungsoo Ahn, Guojie Song, Changhyun Kwon, Kevin Tierney, Lin Xie, Jinkyoo Park |
| 2025 | KDD | Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning. | Qi Li, Zhiguang Cao, Yining Ma, Yaoxin Wu, Yue-Jiao Gong |
| 2025 | KDD | An Efficient Diffusion-based Non-Autoregressive Solver for Traveling Salesman Problem. | Mingzhao Wang, You Zhou, Zhiguang Cao, Yubin Xiao, Xuan Wu, Wei Pang, Yuan Jiang, Hui Yang, Peng Zhao, Yuanshu Li |
| 2025 | WWW | Dual Operation Aggregation Graph Neural Networks for Solving Flexible Job-Shop Scheduling Problem with Reinforcement Learning. | Peng Zhao, You Zhou, Di Wang, Zhiguang Cao, Yubin Xiao, Xuan Wu, Yuanshu Li, Hongjia Liu, Wei Du, Yuan Jiang, Liupu Wang |
| 2025 | SIGIR | LIGHT: Enhancing Learning Path Recommendation via Knowledge Topology-Aware Sequence Optimization. | Xiaoshan Yu, Shangshang Yang, Ziwen Wang, Siyu Song, Haiping Ma, Zhiguang Cao, Xingyi Zhang |
| 2024 | AAAI | GLOP: Learning Global Partition and Local Construction for Solving Large-Scale Routing Problems in Real-Time. | Haoran Ye, Jiarui Wang, Helan Liang, Zhiguang Cao, Yong Li, Fanzhang Li |
| 2024 | ICLR | Deep Reinforcement Learning Guided Improvement Heuristic for Job Shop Scheduling. | Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang |
| 2024 | ICML | MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts. | Jianan Zhou, Zhiguang Cao, Yaoxin Wu, Wen Song, Yining Ma, Jie Zhang, Chi Xu |
| 2024 | ICML | Adaptive Stabilization Based on Machine Learning for Column Generation. | Yunzhuang Shen, Yuan Sun, Xiaodong Li, Zhiguang Cao, Andrew C. Eberhard, Guangquan Zhang |
| 2024 | IJCAI | Cross-Problem Learning for Solving Vehicle Routing Problems. | Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu |
| 2024 | KDD | Hierarchical Neural Constructive Solver for Real-world TSP Scenarios. | Yong Liang Goh, Zhiguang Cao, Yining Ma, Yanfei Dong, Mohammed Haroon Dupty, Wee Sun Lee |
| 2024 | UAI | Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem. | Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun |
| 2023 | ICML | Towards Omni-generalizable Neural Methods for Vehicle Routing Problems. | Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang |
| 2023 | UAI | Multi-view graph contrastive learning for solving vehicle routing problems. | Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang |
| 2022 | AAAI | Learning to Solve Routing Problems via Distributionally Robust Optimization. | Yuan Jiang, Yaoxin Wu, Zhiguang Cao, Jie Zhang |
| 2022 | ICLR | Learning Scenario Representation for Solving Two-stage Stochastic Integer Programs. | Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang |
| 2022 | IJCAI | Efficient Neural Neighborhood Search for Pickup and Delivery Problems. | Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, Yuejiao Gong, Yeow Meng Chee |
| 2022 | KDD | Interpreting Trajectories from Multiple Views: A Hierarchical Self-Attention Network for Estimating the Time of Arrival. | Zebin Chen, Xiaolin Xiao, Yue-Jiao Gong, Jun Fang, Nan Ma, Hua Chai, Zhiguang Cao |
| 2021 | AAAI | Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems. | Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang |
| 2020 | AAAI | AATEAM: Achieving the Ad Hoc Teamwork by Employing the Attention Mechanism. | Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang |
| 2017 | AAAI | Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method. | Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans A. Oliehoek, Ulrich Fastenrath |
| 2016 | AAAI | Multiagent-Based Route Guidance for Increasing the Chance of Arrival on Time. | Zhiguang Cao, Hongliang Guo, Jie Zhang, Ulrich Fastenrath |