59 citations · 165 across the 16 of their papers we have counts for
17 papers
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-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…
A Rollout-Based Algorithm and Reward Function for Resource Allocation in Business Processes
Jeroen Middelhuis, Zaharah Bukhsh, Ivo Adan +1
Resource allocation plays a critical role in minimizing cycle time and improving the efficiency of business processes. Recently, Deep Reinforcement Learning (DRL) has emerged as a…
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…
Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
Lisandro A. Jimenez-Roa, Thiago D. Simão, Zaharah Bukhsh +4
Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various…
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…
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…
Job Shop Scheduling Benchmark: Environments and Instances for Learning and Non-learning Methods
Robbert Reijnen, Igor G. Smit, Hongxiang Zhang +3
Job shop scheduling problems address the routing and sequencing of tasks in a job shop setting. Despite significant interest from operations research and machine learning communiti…
Unsupervised anomaly detection algorithms on real-world data: how many do we need?
Roel Bouman, Zaharah Bukhsh, Tom Heskes
In this study we evaluate 32 unsupervised anomaly detection algorithms on 52 real-world multivariate tabular datasets, performing the largest comparison of unsupervised anomaly det…
Learning policies for resource allocation in business processes
J. Middelhuis, R. Lo Bianco, E. Scherzer +3
Efficient allocation of resources to activities is pivotal in executing business processes but remains challenging. While resource allocation methodologies are well-established in…
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement Learning
Robbert Reijnen, Yingqian Zhang, Hoong Chuin Lau +1
The Adaptive Large Neighborhood Search (ALNS) algorithm has shown considerable success in solving combinatorial optimization problems (COPs). Nonetheless, the performance of ALNS r…
On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors
Zaharah Bukhsh, Aaqib Saeed
Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of p…
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning
Zaharah A. Bukhsh, Nils Jansen, Hajo Molegraaf
Cost-effective asset management is an area of interest across several industries. Specifically, this paper develops a deep reinforcement learning (DRL) solution to automatically de…
ProcessTransformer: Predictive Business Process Monitoring with Transformer Network
Zaharah A. Bukhsh, Aaqib Saeed, Remco M. Dijkman
Predictive business process monitoring focuses on predicting future characteristics of a running process using event logs. The foresight into process execution promises great poten…
Damage detection using in-domain and cross-domain transfer learning
Zaharah A. Bukhsh, Nils Jansen, Aaqib Saeed
We investigate the capabilities of transfer learning in the area of structural health monitoring. In particular, we are interested in damage detection for concrete structures. Typi…