This study presents a Fuzzy Petri Net (FPN) based dynamic decision-making model for timely cleaning of solar collectors, aimed at optimizing schedules to avoid re-soiling. The model integrates Fuzzy Production Rules (FPR) for visual rule representation and combines expert system derived rules with human expert insights for informed Cleaning Decisions (CD). Its main goal is to enhance solar collector reflectance, often reduced by dust, through effective cleaning. Understanding FPN’s principles is critical to grasping the model’s impact, which dynamically adapts to environmental changes. This study aims to provide a comprehensive strategy for maintaining the cleanliness of mirrors over an extended period, ensuring they remain pristine and functional. Despite the challenges of vague rules, the model achieves satisfactory outcomes, guided by human experts and expert system-experienced judgment.