Hybridization of Particle Swarm Optimization with Variable Neighborhood Search and Simulated Annealing for Improved Handling of the Permutation Flow-Shop Scheduling Problem

Permutation flow-shop scheduling is the strategy that ensures processing of jobs on each subsequent machine in exact same order while optimizing an objective, which generally minimization makespan. Because its NP-Complete nature, a substantial portion literature has mainly focused computational efficiency and development different AI-based hybrid techniques. Particle Swarm Optimization (PSO) also been frequently used for this purpose recent past. Following trend to further explore capabilities PSO, first, standard PSO was developed during research, then hybridized with Variable Neighborhood Search (PSO-VNS) later Simulated Annealing (PSO-VNS-SA) handle Flow-Shop Scheduling Problems (PFSP). The effect hybridization validated through internal comparison based results 120 instances devised by Taillard variable problem sizes. Moreover, other reported metaheuristics proved (HPSO) research performed exceedingly well. A smaller value 0.48 ARPD (Average Relative Performance Difference) algorithm evidence robust nature significantly improved performance makespan as compared algorithms.