Reliability Enhancement in Automotive Manufacturing Via Cost-Constrained Particle Swarm Optimization

Abstract

Nature-inspired algorithms have emerged as powerful and versatile tools for solving real-world optimization problems. This study applies Particle Swarm Optimization (PSO) to address the cost-constrained reliability optimization problem in automotive manufacturing systems. The proposed approach determines the optimal reliability configuration and the required number of redundant components within the plant while adhering to a specified cost constraint. The mathematical formulation, objective function, and relevant constraints for the problem are systematically analyzed. Results demonstrate the effectiveness of PSO in achieving high system reliability within budgetary limitations, offering valuable insights for the design and management of reliable automotive manufacturing operations.