Optimizing Resource Allocation Policies in Real-World Business Processes Using Hybrid Process Simulation and Deep Reinforcement Learning. - Francesca Meneghello 002; Jeroen Middelhuis; Laura Genga; Zaharah Bukhsh; Massimiliano Ronzani; Chiara Di Francescomarino; Chiara Ghidini; Remco M. Dijkman (BPM, 2024)

Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization. - Jesse van Remmerden; Maurice Kenter; Diederik M. Roijers; Charalampos Andriotis; Yingqian Zhang 001; Zaharah Bukhsh (CoRR, 2024)

Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? - Roel Bouman; Zaharah Bukhsh; Tom Heskes (J. Mach. Learn. Res., 2024)

Graph neural networks for job shop scheduling problems: A survey. - Igor G. Smit; Jianan Zhou 002; Robbert Reijnen; Yaoxin Wu; Jian Chen 022; Cong Zhang; Zaharah Bukhsh; Yingqian Zhang 001; Wim Nuijten (Comput. Oper. Res., 2025)

Learning policies for resource allocation in business processes. - Jeroen Middelhuis; Riccardo Lo Bianco; Eliran Sherzer; Zaharah Bukhsh; Ivo Adan; Remco M. Dijkman (Inf. Syst., 2025)

Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization. - Robbert Reijnen; Yaoxin Wu; Zaharah Bukhsh; Yingqian Zhang 001 (CoRR, 2025)

Offline reinforcement learning for learning to dispatch for job shop scheduling. - Jesse van Remmerden; Zaharah Bukhsh; Yingqian Zhang 001 (Mach. Learn., 2025)