Sustainable Transportation Optimisation of Waste Electrical and Electronic Equipment Using AI-Based Evolutionary Algorithms

Waste Electrical and Electronic Equipment (WEEE) management is a critical global challenge. This study proposes a model for the WEEE Transportation Problem using advanced evolutionary algorithms such as the Genetic Algorithm (GA), the Offspring-Selected Genetic Algorithm (OSGA), the Evolution Strategy (ES), and the Offspring-Selected Evolution Strategy (OSES). These algorithms, which are part of the field of Artificial Intelligence (AI), are applied to optimise transportation routes, minimising time and costs, and promoting sustainability by reducing the carbon footprint. Test instances and solutions are presented to demonstrate the feasibility of the model and the effectiveness of the proposed algorithms. Rather than providing technical detail, the focus is placed on the novelty of applying these algorithms to the WEEE Transportation Problem in Mexico, particularly for minimising operational cost. While reductions in carbon emissions are discussed as a natural consequence of cost optimisation, a formal dual-objective formulation is beyond the present scope and is identified as a direction for future work.