Hybrid Optimization for Job-Shop Scheduling: Enhancing Efficiency and Stability with JFO-AFO-SA

A crucial optimization challenge in manufacturing, cloud computing, and parallel processing, is job-shop scheduling (JSS), where reducing makespan increases productivity and resource usage. Conventional scheduling techniques, such as standalone metaheuristics and precise algorithms, frequently have computational complexity and premature convergence. To address these challenges, this research suggests a hybrid optimization strategy (JFO-AFO-SA) that combines Simulated Annealing (SA) for fine-tuning optimization, Archerfish Optimization (AFO) for guided local search, and Jellyfish Optimization (JFO) for global exploration. The performance of the hybrid algorithm was compared with stand-alone JFO-AFO and SA techniques and assessed using Taillard's JSS datasets. The results show that maintaining faster convergence and better stability hybrid algorithm lowers makespan by an average of 15% compared to SA and 6% compared to JFO-AFO, with maximum improvements reaching 19.88% and 10.42%, respectively, in large-scale instances. The proposed hybrid approach enhances solution quality and robustness, with potential applications in real-time dynamic scheduling and large-scale industrial optimization.