Journal of Resource Management and Decision Engineering In Press 2027 03 01 Design of a Fuzzy Multi-Objective Mathematical Model for Short-Term Preventive Maintenance Scheduling with a Total Productive Maintenance Approach Design of a Fuzzy Multi-Objective Mathematical Model for Short-Term Preventive Maintenance Scheduling with a Total Productive Maintenance Approach 1 17 EN Journal Article 2026 02 09 <p class="Abstract"><span lang="EN-GB" style="letter-spacing: -.2pt;">The increasing energy consumption in the industrial sector and the consequences associated with greenhouse gas emissions have intensified the need for innovative approaches that simultaneously manage production, cost, and environmental considerations. In this context, production planning and machine scheduling, together with preventive maintenance, play a significant role in reducing energy consumption, increasing productivity, improving quality, and minimizing unexpected downtime. Accordingly, the present study was conducted with the aim of developing a comprehensive model for the simultaneous optimization of production and maintenance processes under uncertainty. This research is applied in nature and proposes a multi-objective mathematical model for production scheduling and preventive maintenance implementation. The primary objectives of the proposed model include minimizing production completion time, reducing costs, decreasing environmental pollutant emissions, and maximizing product quality. Furthermore, to better reflect real-world conditions, uncertainty in model parameters is addressed through a fuzzy approach. To solve the model, an exact optimization method was employed in GAMS software for small-sized problems, while the NSGA-II metaheuristic algorithm was implemented in the MATLAB environment for large-scale problems. The results demonstrated that the proposed approach effectively balances the conflicting objectives of the problem and is capable of generating efficient and reliable solutions. Moreover, the performance evaluation of the NSGA-II algorithm using indicators such as the number of Pareto solutions, solution quality, distance from the ideal point, and solution diversity indicated that the algorithm exhibits desirable efficiency and robustness in large-scale instances. Overall, the findings suggest that integrating production planning with preventive maintenance while incorporating environmental considerations can improve the performance of manufacturing systems and contribute to the advancement of sustainable production.</span></p> https://www.journalrmde.com/index.php/jrmde/article/download/349/351