An Intelligent Decision Support Model for Sustainable Automated E-fulfillment Centers Through Fuzzy Association Rules Mining

Recently, the logistics sector has come to recognize the importance of sustainability, especially with regard to automated e-fulfillment centers that mainly rely on automation and robotics technology. Practitioners are increasingly recognizing the importance of efficiently maintaining order fulfillment while minimizing the costs associated with renewable energy. Thus, practitioners need decisional support to optimize their renewable energy resources and prevent wastage, all while ensuring effective operations. This study uses fuzzy association rules mining (FARM) to reveal links that are hidden between environmental data and the production of renewable energy. “IF-then” rules will be produced by the FARM, providing decision support in the field. The proposed model focuses on solar power as a case study. By utilizing the extracted rules, users can identify the most influential factors that impact solar power generation. By targeting those crucial factors, practitioners can effectively determine the ideal quantity of solar panels, allowing them to prevent the accumulation of unused solar panels while still maintaining optimal operational performance.