Goal: This research aims to develop a novel scheduling model integrating the intelligent product paradigm of a product-driven system (PDS) with the shifting bottleneck heuristic (SBH) to enhance rescheduling efficiency and adaptability in dynamic job shop scheduling problems with disruptions (JSSP-D). Design / Methodology / Approach: The model employs agent-based modeling, where products act as autonomous agents in rescheduling decisions. Simulations covered 151 scenarios across 14 benchmark instances of machine failures, with production time increases of 100%, 200%, and 300%. The model’s performance was evaluated on its ability to minimize makespan deterioration and maintain efficiency under different disturbance levels. Results: The PDS-SBH model effectively reduced production efficiency gaps, achieving an average makespan reduction of 7.81%, with peaks of 36.06%. Higher disturbance levels allowed for better rescheduling outcomes, albeit with increased variability. The model’s adaptability provided solutions comparable or superior to stable scheduling benchmarks. Limitations of the investigation: The study used 14 benchmark instances and focused solely on machine failures, limiting generalizability to other disruptions like resource shortages or order changes. Despite these constraints, the 151 scenarios and rigorous analysis strengthen result reliability. Practical implications: The PDS-SBH model offers a robust approach for real-time schedule adjustments, maintaining operational continuity, and optimizing resource use. It provides practical insights for decision support systems and policy development in dynamic manufacturing. Originality / Value: This study pioneers a hybrid approach combining intelligent product paradigms with SBH. It advances JSSP-D research by presenting a resilient, adaptive framework for dynamic scheduling, significantly contributing to manufacturing efficiency and robustness.
Sáez et al. (Thu,) studied this question.