A Koch Snowflake-Based Fuzzy Decision Framework for Solar Panel Waste Management Strategies in Food Manufacturing Systems

Renewable energy technologies, particularly solar panels, are increasingly adopted in food manufacturing and processing facilities to enhance energy efficiency and reduce environmental impacts. However, their limited operational lifespan leads to the generation of significant amounts of waste, which may create environmental risks and economic losses if not managed effectively. Despite the growing importance of solar energy systems in energy intensive food production environments, comprehensive and systematic analyses identifying the key factors and optimal strategies for improving solar panel waste management investments remain limited in the literature, potentially resulting in inefficient resource allocation and suboptimal decision making in the food industry. To address this gap, this study proposes a novel fuzzy multi criteria decision making framework to evaluate and prioritize effective solar panel waste management strategies within food production systems by integrating the Compromise Improvement Measure for Alternatives under Subjectivity method, the Compromise Ranking of Alternatives from Distance to Ideal Solution method, and a dimensionality reduction technique based on Koch snowflake fuzzy sets. The proposed approach utilizes the fractal-based structure of Koch snowflake fuzzy sets to provide a more flexible representation of uncertainty compared to conventional fuzzy methods. The empirical results indicate that technology and infrastructure is the most influential criterion with a weight of 0.248, followed by establishing traceable logistics and processes with a weight of 0.240 and selecting recyclable raw materials with a weight of 0.166. In contrast, awareness and training and easy access to financial resources receive relatively lower importance weights of 0.103 and 0.092 respectively. Regarding investment strategies, recycling emerges as the most effective alternative, followed by collection, disposal, and separation. These findings highlight the critical role of technological capacity and recycling oriented investments in improving the sustainability and efficiency of solar panel waste management systems in food manufacturing environments. The analysis is based on evaluations obtained from eight experts working in solar energy, photovoltaic waste management, and sustainability related industrial operations associated with food manufacturing environments. The proposed framework is applied as an expert based decision support model for evaluating solar panel waste management strategies under uncertainty. However, the study is limited by the relatively small expert panel and the absence of large scale operational plant level datasets.