Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks. - Mahdi Mohammadizadeh; Arash Mozhdehi; Yani Ioannou; Xin Wang 004 (Canadian AI, 2023)

Edge-DIRECT: A Deep Reinforcement Learning-based Method for Solving Heterogeneous Electric Vehicle Routing Problem with Time Window Constraints. - Arash Mozhdehi; Mahdi Mohammadizadeh; Xin Wang 004 (Canadian AI, 2024)

EFECTIW-ROTER: Deep Reinforcement Learning Approach for Solving Heterogeneous Fleet and Demand Vehicle Routing Problem With Time-Window Constraints. - Arash Mozhdehi; Mahdi Mohammadizadeh; Yunli Wang; Sun Sun 002; Xin Wang 004 (SIGSPATIAL/GIS, 2024)

SPADE: Solving the Multi-Depot Vehicle Routing Problem with Inter-Depot Routes Using Multi-Agent Deep Reinforcement Learning. - Arash Mozhdehi; Mahdi Mohammadizadeh; Saeid Kalantari; Beom Sae (Shawn) Kim; Xin Wang 004 (Canadian AI, 2025)