13 citations · 36 across the 27 of their papers we have counts for
29 papers
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…
When does learning pay off? A study on DRL-based dynamic algorithm configuration for carbon-aware scheduling
Andrea Mencaroni, Robbert Reijnen, Yingqian Zhang +1
Deep reinforcement learning (DRL) has recently emerged as a promising tool for Dynamic Algorithm Configuration (DAC), enabling evolutionary algorithms to adapt their parameters onl…
Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks
Minshuo Li, Yaoxin Wu, Pavel Troubil +2
Complex real-world optimization problems often involve both discrete decisions and nonlinear relationships between variables. Many such problems can be modeled as polynomial-object…
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization
Xia Jiang, Jing Chen, Cong Zhang +5
While large language models (LLMs) have shown strong performance in math and logic reasoning, their ability to handle combinatorial optimization (CO) -- searching high-dimensional…
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…
Large Language Models as End-to-end Combinatorial Optimization Solvers
Xia Jiang, Yaoxin Wu, Minshuo Li +2
Combinatorial optimization (CO) problems, central to decision-making scenarios like logistics and manufacturing, are traditionally solved using problem-specific algorithms requirin…
Pair-Bid Auction Model for Optimized Network Slicing in 5G RAN
Mengyao Li, Sebastian Troia, Yingqian Zhang +1
Network slicing is a key 5G technology that enables multiple virtual networks to share physical infrastructure, optimizing flexibility and resource allocation. This involves Mobile…
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…
Multimodal Attention-Aware Fusion for Diagnosing Distal Myopathy: Evaluating Model Interpretability and Clinician Trust
Mohsen Abbaspour Onari, Lucie Charlotte Magister, Yaoxin Wu +10
Distal myopathy represents a genetically heterogeneous group of skeletal muscle disorders with broad clinical manifestations, posing diagnostic challenges in radiology. To address…
Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization
Robbert Reijnen, Yaoxin Wu, Zaharah Bukhsh +1
Deep reinforcement learning (DRL) has been widely used for dynamic algorithm configuration, particularly in evolutionary computation, which benefits from the adaptive update of par…
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…
Neural Combinatorial Optimization for Stochastic Flexible Job Shop Scheduling Problems
Igor G. Smit, Yaoxin Wu, Pavel Troubil +2
Neural combinatorial optimization (NCO) has gained significant attention due to the potential of deep learning to efficiently solve combinatorial optimization problems. NCO has bee…
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…
Bridging Large Language Models and Optimization: A Unified Framework for Text-attributed Combinatorial Optimization
Xia Jiang, Yaoxin Wu, Yuan Wang +1
To advance capabilities of large language models (LLMs) in solving combinatorial optimization problems (COPs), this paper presents the Language-based Neural COP Solver (LNCS), a no…
Graph Neural Networks for Job Shop Scheduling Problems: A Survey
Igor G. Smit, Jianan Zhou, Robbert Reijnen +6
Job shop scheduling problems (JSSPs) represent a critical and challenging class of combinatorial optimization problems. Recent years have witnessed a rapid increase in the applicat…
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
Jesse van Remmerden, Maurice Kenter, Diederik M. Roijers +3
In this paper, we introduce Multi-Objective Deep Centralized Multi-Agent Actor-Critic (MO- DCMAC), a multi-objective reinforcement learning (MORL) method for infrastructural mainte…
Learning Efficient and Fair Policies for Uncertainty-Aware Collaborative Human-Robot Order Picking
Igor G. Smit, Zaharah Bukhsh, Mykola Pechenizkiy +3
In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pick…
Cross-Problem Learning for Solving Vehicle Routing Problems
Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou +4
Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP va…
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…