Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem

arXiv:2402.17606v1 Announce Type: new
Abstract: Existing learning-based methods for solving job shop scheduling problem (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 attention network (TBGAT), a novel GNN architecture based on the attention mechanism, to embed the DG for solving JSSP in a local search framework. Specifically, TBGAT embeds the DG from a forward and a backward view, respectively, …

abstract arxiv attention cs.ai cs.lg gnn graph graphs job network paper scheduling topology type