## 5 Conclusions  
This study introduces nighttime light remote sensing data and integrates traditional urban metrics, such as GDP and per capita consumption, to construct an innovative indicator system for accurately predicting waste generation in Beijing. Six machine learning regression algorithms--linear regression, support vector machines, random forests, elastic net, Bayesian ridge regression, and gradient boosting regression--were employed for modeling. Experimental results indicate that the gradient boosting regression algorithm exhibited the best predictive performance, achieving an R\({}^{2}\) value of 0.99 and a prediction accuracy of 92%. This high-precision model provides a solid data-driven foundation for forecasting future waste production in Beijing and optimizing resource allocation. Furthermore, this research models Beijing's municipal waste collection and transportation system as a multi-objective location-routing problem, considering three key optimization objectives: customer residential experience, cost efficiency, and environmental impact. By incorporating fluctuations in household waste generation and normally distributed stochastic perturbations, the model enhances its realism. Case studies in the boundary areas between Fangshan District and Tongzhou District, utilizing road network data and Google Maps, identified potential site centers and service points, establishing a high-quality dataset for waste generation predictions. To address the location-routing issue effectively, we applied multi-objective evolutionary algorithms (MOEAs) and the non-dominated sorting genetic algorithm II (NSGA-II) for optimization design. The solutions proposed by the NSGA-II algorithm excelled in terms of environmental sustainability, economic savings, and resident satisfaction. These optimized solutions provide robust support for refining Beijing's municipal waste collection and transportation system, enhancing waste management efficiency, reducing resource wastage, and improving overall operational effectiveness. Moreover, the models and optimization strategies presented in this study offer scientific decision-making foundations for government agencies and relevant departments. They facilitate more effective policy measures and serve as references for other cities facing similar challenges. Specifically, this study demonstrates how data-driven decision-making methods can be applied within urban governance contexts to achieve rational resource distribution, lower costs, and enhance residents' quality of life, thereby mitigating environmental pollution and promoting sustainable urban development. In summary, this study makes significant contributions to the scientific rigor and sustainability of urban waste management practices. It not only enhances the precision of waste generation predictions but also provides practical, data-driven solutions for optimizing waste collection and transportation systems, ultimately contributing to more efficient and sustainable urban management. Methodology, J.X. ; Software, J.X. ; Validation, J.X. ; Formal analysis, Y.L. ; Investigation, Y.L. ; Data curation, G.Z. ; Writing--original draft, J.X. ; Writing--original draft, H.Y. ; Writing--review & editing, G.Z. ; Visualization, Y.L. ; Supervision, G.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Fundamental Research Funds for the Central Universities, China, grant number 2652019028. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Conflicts of Interest:** The authors declare no conflict of interest.