Joint Optimization of Part Quality Inspection Planning, Buffer Allocation, and Preventive Maintenance in a Serial Manufacturing

This paper addresses the integration of quantity, quality, and maintenance challenges in a serial multistage manufacturing system. This research presents a comprehensive joint optimization model that extensively considers the interdependencies among these aspects. The model encompasses buffer allocation, which efficiently manages stochastic processing times, part quality inspection planning to detect and minimize defects, and preventative maintenance strategies to mitigate machine deterioration. By integrating these functions, the manufacturing system can significantly improve its overall performance and reduce costs, with research results showing an average cost reduction of 15%. The proposed model formulates a stochastic mixed integer nonlinear programming problem, which is NP-hard and combinatorial. To address this problem, a comprehensive solution approach is employed, combining a genetic algorithm as the primary generative method with hybrid genetic algorithm variations. Additionally, simulation (recursive approach) serves as the evaluative method. Computational experiments are conducted to validate and optimize the proposed model. The findings make a substantial contribution to the existing body of knowledge, offering a robust solution that enhances performance and reduces costs in addressing quantity, quality, and maintenance challenges in a serial multistage manufacturing system. Finally, a discussion of the research results, their implications, and applications offers valuable insights for practitioners and decision-makers aiming to optimize their production processes.