Particle Swarm Optimization with the Swapping Scheduling Neighborhood Algorithm for the Flow Shop Scheduling Problem: Minimizing Makespan and Improving Solution Quality

This paper presents a comprehensive study on the Flow Shop Scheduling Problem (FSSP), a critical challenge in operational management. The FSSP is mathematically formulated with the objective of minimizing the makespan, a vital performance metric representing the total completion time of all tasks. To address the FSSP, a novel approach called Particle Swarm Optimization with the Swapping Scheduling Neighborhood (PSOSSN) algorithm is proposed. By integrating the Swapping Scheduling Neighborhood (SSN) algorithm with Particle Swarm Optimization (PSO), our model aims to optimize the job sequence and reduce production time, which represents the maximum completion time of jobs processed on the same machines within flow shops. The SSN scheme's incorporation improves the particle's local search ability, leading to improved optimization performance. A comparative analysis was conducted between SSN, PSO, and PSOSSN in terms of makespan and computation time metrics. Our findings indicate that PSOSSN is more efficient in terms of makespan optimization but imposes considerable computational burdens. To overcome this challenge, a balance between solution quality and computational efficiency is sought by reducing the number of particles and iterations in PSOSSN to achieve satisfactory solutions within acceptable computational time limits.