Abstract: The Distributed Flexible Job Shop Scheduling Problem (DFJSP) is critical in modern manufacturing; however, existing research has not sufficiently addressed dynamic disturbances, particularly unexpected worker absences. This study extends the DFJSPW model to introduce an enhanced framework, DFJSPWA, which optimizes maximum makespan, worker workload, and total energy consumption by integrating worker load factors with random absenteeism. To solve this complex problem, we propose a Q-learning-based Hyper-heuristic Evolutionary Algorithm (QLHHEA). This algorithm features a segmented encoding scheme that implicitly captures absenteeism information, utilizing a decoding process tailored for both standard and rescheduling contexts. Additionally, we construct a pool of twelve efficient Low-Level Heuristics (LLHs) combined with Q-learning to enable the adaptive selection of operators. Furthermore, a Hybrid Rescheduling Method (HRM) is developed, employing three response strategies based on worker status and the urgency of the absenteeism. Comprehensive experiments on 58 benchmark instances demonstrate that QLHHEA significantly outperforms six established algorithms, including MOEA/D and NSGA-II. Statistical validation confirms the superiority of the proposed method. This research provides a robust theoretical and methodological framework for improving scheduling efficiency and resource utilization in distributed production systems facing worker absenteeism.

Key words: Worker absenteeism')">Worker absenteeism, Distributed flexible job shop scheduling problem, Hyper-heuristic evolutionary algorithm, Dynamic rescheduling, Q-learning