13 citations · 24 across the 11 of their papers we have counts for
3 papers · 1 filter
RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
Xiangjie Xiao, Cong Zhang, Wen Song +1
Neural approaches to the Flexible Job Shop Scheduling Problem (FJSP), particularly those based on deep reinforcement learning (DRL), have gained growing attention in recent years.…
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Cong Zhang, Zhiguang Cao, Yaoxin Wu +2
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 meaningf…
Automated Reinforcement Learning: An Overview
Reza Refaei Afshar, Joaquin Vanschoren, Uzay Kaymak +4
Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes. RL model…