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

Automated summary

Permutation flow-shop scheduling is a strategy that optimizes the processing of jobs while ensuring the same order on subsequent machines. Particle Swarm Optimization (PSO) has been frequently used for this purpose. This research developed a standard PSO and hybridized it with Variable Neighborhood Search (PSO-VNS) and Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The hybrid PSO (HPSO) performed well compared to other algorithms, with an ARPD value of 0.48 indicating robustness and improved performance in optimizing makespan.