INTEGRATED MODELING OF PRODUCTION, MAINTENANCE, QUALITY, AND INVENTORY IN OPEN SHOPS CONSIDERING UNCERTAINTY AND HUMAN LEARNING

Abstract

In complex manufacturing environments, simultaneous decision-making in production and maintenance domains is of great importance, since the overall performance of the system is strongly influenced by the interaction among production planning, resource allocation, and maintenance policies. In this study, a multi-objective mathematical model is developed for production and maintenance scheduling in open shop systems, pursuing three main objectives simultaneously: minimizing the total system cost, reducing the makespan, and minimizing downtime caused by equipment failures. To solve this model, the NSGA-II algorithm is employed as an effective metaheuristic approach for multi-objective optimization. Moreover, three scenarios are designed and implemented to analyze model behavior across different system sizes. The obtained results indicate that as the system scale increases, both the average and dispersion of objectives rise, reflecting higher complexity and greater sensitivity of managerial decisions in larger systems. Furthermore, Pareto front analysis reveals the inherent conflict among objectives, as cost reduction is generally accompanied by increased downtime. Nevertheless, the presence of clustered regions on the Pareto front highlights balanced and efficient trade-off solutions. This research can serve as a decision support tool in real manufacturing environments and provide a foundation for the development of more advanced models in the future.