In modern manufacturing, production scheduling, maintenance planning, and quality control are inextricably linked, yet they are often managed in isolation. This paper proposes an integrated mathematical framework that synchronizes these three critical functions to minimize total operational costs. Unlike traditional models that assume a constant failure rate, this study utilizes Non-Homogeneous Poisson Processes (NHPP) to more accurately reflect the time-dependent deterioration of equipment and its impact on process quality. The model accounts for diverse operational scenarios, including unexpected failures, false alarms, and planned maintenance, while strictly adhering to real-world constraints such as system availability and maximum job tardiness. Leveraging Particle Swarm Optimization (PSO) to navigate the complex decision space, the proposed approach was validated through sensitivity analyses and industrial case studies in the food and automotive sectors. The results demonstrate that this unified optimization strategy significantly outperforms sequential planning methods, providing a robust tool for enhancing reliability, cost-efficiency, and delivery performance in competitive manufacturing environments.

Keywords

Integrated Optimization, Production Scheduling, Condition-based Maintenance, Statistical Process Control and Particle Swarm Optimization (PSO).