Hybrid Model for Job-Shop Scheduling with Maintenance Constraints

This technical report presents a hybrid model designed to address the job-shop scheduling problem, incorporating preventive maintenance, sequence-dependent setup times, and unknown processing times. The authors, Joep Ooms and Alexander Hubl, outline the limitations of traditional job-shop scheduling approaches that assume all processing times are known in advance. Instead, this study introduces a more realistic scenario where processing times are revealed only as products arrive at machines. The hybrid model combines discrete event simulation with optimization techniques, utilizing meta-heuristics such as genetic algorithms, ant colony optimization, and simulated annealing. The report documents the methodology and results of applying this model, demonstrating significant improvements in scheduling efficiency compared to random job sequencing. The inclusion of preventive maintenance and sequence-dependent setup times is emphasized as critical factors for enhancing production throughput in real-world manufacturing environments. This research contributes to the literature by bridging gaps in existing scheduling methodologies that often overlook stochastic elements in job-shop settings.