13 citations · 36 across the 27 of their papers we have counts for
12 papers · 1 filter
Multi-Term Fourier Graph Neural Network with Sample Relationship Learning for Enhanced Remaining Useful Life Prediction
Ya Song, Laurens Bliek, Yaoxin Wu +1
Predicting the remaining useful life (RUL) is essential for effective predictive maintenance. Spatio-Temporal Graph Neural Networks (ST-GNNs), which can model both temporal and spa…
Learning with Foresight: Enhancing Neural Routing Policy via Multi-Node Lookahead Prediction
Xia Jiang, Yaoxin Wu, Yew-Soon Ong +1
Neural policies have shown promise in solving vehicle routing problems due to their reduced reliance on handcrafted heuristics. However, current training paradigms suffer from a fu…
DiPRL: Learning Discrete Programmatic Policies via Architecture Entropy Regularization
Chengpeng Hu, Yingqian Zhang, Hendrik Baier
Programmatic reinforcement learning (PRL) offers an interpretable alternative to deep reinforcement learning by representing policies as human-readable and -editable programs. Whil…
Scheduling That Speaks: An Interpretable Programmatic Reinforcement Learning Framework
Chengpeng Hu, Yingqian Zhang, Hendrik Baier
Deep reinforcement learning (DRL) has recently emerged as a promising approach to solve combinatorial optimization problems such as job shop scheduling. However, the policies learn…
Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
Wei Liu, Yaoxin Wu, Yingqian Zhang +2
Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs). Nevertheless, the robustness of these learning…
End-to-end Deep Reinforcement Learning for Stochastic Multi-objective Optimization in C-VRPTW
Abdo Abouelrous, Laurens Bliek, Yaoxin Wu +1
In this work, we consider learning-based applications in routing to solve a Vehicle Routing variant characterized by stochasticity and multiple objectives. Such problems are repres…
Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies thr…
Graph Reduction with Unsupervised Learning in Column Generation: A Routing Application
Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor +2
Column Generation (CG) is a popular method dedicated to enhancing computational efficiency in large scale Combinatorial Optimization (CO) problems. It reduces the number of decisio…
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing
Abdo Abouelrous, Laurens Bliek, Adriana F. Gabor +2
In this paper, we address the problem of Column Generation (CG) using Reinforcement Learning (RL). Specifically, we use a RL model based on the attention-mechanism architecture to…
Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling
Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
The Job Shop Scheduling Problem (JSSP) is a complex combinatorial optimization problem. While online Reinforcement Learning (RL) has shown promise by quickly finding acceptable sol…
Parcel loss prediction in last-mile delivery: deep and non-deep approaches with insights from Explainable AI
Jan de Leeuw, Zaharah Bukhsh, Yingqian Zhang
Within the domain of e-commerce retail, an important objective is the reduction of parcel loss during the last-mile delivery phase. The ever-increasing availability of data, includ…
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…