Hybrid Genetic Algorithm and Particle Swarm Optimization for Enhanced Facility Layout Design

Abstract

Efficient facility layout design is essential for optimizing production efficiency, reducing material handling costs, and ensuring effective space utilization in industrial and manufacturing settings. However, the complexity of multi-objective layout design, which involves balancing multiple conflicting criteria, poses a significant challenge for traditional optimization methods. This paper introduces a hybrid approach that integrates Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to address the limitations of standalone optimization techniques in facility layout problems. The proposed hybrid GA-PSO model capitalizes on GA's exploration ability to generate a diverse set of layout configurations, while PSO refines these initial solutions through swarm intelligence principles, achieving a balance between exploration and exploitation in the search space. GA is first employed to produce an initial population, applying crossover and mutation operators to explore potential solutions. Subsequently, PSO enhances these solutions by iteratively adjusting particle velocities based on individual and global best positions, converging towards an optimized layout configuration. This approach is validated through a series of case studies in manufacturing facility layout design, demonstrating superior performance in minimizing material handling costs, improving workflow efficiency, and optimizing spatial arrangements when compared to traditional GA or PSO models alone. The findings underscore the practicality and effectiveness of the GA-PSO hybrid model, providing a feasible and robust solution for facility layout optimization, and offering engineering managers a powerful tool for complex layout planning and operational decision-making.