Abstracts

The aim of this paper is the development and simulation of an intelligent Decision Support System (DSS) in the field of waste recycling. The proposed model integrates the Random Forest algorithm for classification and prediction with an optimization model that minimizes costs and maximizes revenues from the sale of recycled materials. The database provides input parameters (waste type, quantity, processing cost, revenue), while the user interface enables interaction with the system and the generation of recommendations. The simulation was conducted on hypothetical data for three types of waste (plastic, glass, and metal), applying different recycling strategies. The results demonstrate that the DSS model achieves a positive financial effect, as revenues from sales exceed processing costs, with the greatest contribution observed in the case of metal. These findings confirm that the proposed DSS model can provide economically sustainable recommendations for waste management and serve as a foundation for future research that would include additional waste categories, environmental indicators, and logistical constraints.

Keywords

Decision Support System (DSS), Random Forest, Optimization model, Waste recycling

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