Learning topological representations with bidirectional graph attention network for solving job shop scheduling problem

• Main Authors: ZHANG, Cong, CAO, Zhiguang, WU, Yaoxin, SONG, Wen, SUN, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Data-driven optimization; deep reinforcement learning; job shop scheduling; graph neural network; neural heuristics; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9331; https://ink.library.smu.edu.sg/context/sis_research/article/10331/viewcontent/3_Learning_Topological_Represe.pdf
• Institution: Singapore Management University

Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs). This paper proposes the topology-aware bidirectional graph attent...