A Study on the Optimal Flexible Job-Shop Scheduling with Sequence-Dependent Setup Time Based on a Hybrid Algorithm of Improved Quantum Cat Swarm Optimization

Multi-item and small-lot-size production modes lead to frequent setup, which involves significant setup times has a substantial impact on productivity. In this study, we investigated the optimal flexible job-shop scheduling problem with sequence-dependent time. We built mathematical model objective of minimization maximum completion time (makespan). Considering process sequence, is influenced by time, processing machine load limitations, first, machinery chosen based then tasks are scheduled An improved quantum cat swarm optimization (QCSO) algorithm proposed solve problem, coding method introduced, bit (Q-bit) (CSO) combined, cats iteratively updated rotation angle position; then, dynamic mixture ratio (MR) value selected according number iterations. The use expands our understanding space increases operation efficiency speed. Finally, QCSO parallel genetic (PGA) compared through simulation experiments. results show that better results, robustness improved.