59 citations · 165 across the 16 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…
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…