## 1 Introduction  
As the global economy undergoes rapid development and urbanization accelerates, urban population density has significantly increased, resulting in complex challenges associated with managing municipal solid waste (MSW) [1]. A central issue in this context is the uncertainty surrounding waste generation, which complicates effective vehicle routing and site selection for treatment facilities [2]. Accurately predicting waste generation is essential for achieving environmental protection and promoting sustainable social development [3].  
This study specifically addresses inaccuracies in predictions of waste generation as well as limitations within existing optimization frameworks related to urban solid waste management. We introduce nighttime light remote sensing data as an innovative tool to enhance the accuracy of predictions regarding urban solid waste generation. By denoising this remote sensing data and integrating it with traditional urban datasets, we have developed a novel indicator system along with predictive models. Our application of gradient boosting regression algorithms has achieved a prediction accuracy rate of 92% within the context of Beijing.  
Moreover, to address the high costs associated with recycling route planning and facility siting, we propose an optimization framework grounded in precise predictions of waste generation. The algorithms for solving multi-objective problems can be broadly categorized into two types: traditional optimization methods and heuristic evolutionary algorithms. Traditional optimization methods are mathematical programming techniques based on operations research theory, primarily including constraint programming and dynamic programming algorithms. These approaches address multi-objective problems by averaging multiple objectives and transforming them into lower-dimensional issues. However, this method often struggles to effectively balance the various objectives, making it challenging to achieve optimal solutions for multi-objective optimization problems. In contrast, heuristic evolutionary algorithms leverage evolutionary mechanisms to simulate the chromosomal crossover processes of biological organisms in order to search for optimal solutions. These algorithms are particularly effective in addressing high-complexity models, as they can simultaneously handle multiple objectives and identify a set of optimal solutions known as the Pareto front. In this study, we have selected classical multi-objective evolutionary algorithms (MOEAs) and the non-dominated sorting genetic algorithm II (NSGA-II) for optimization purposes. These evolutionary algorithms are well-suited for complex multi-objective problems and provide robust performance in seeking balance and optimal solutions.  
A distinctive feature of our research is the assumption that waste generation at service points is not a fixed value but rather follows a normal distribution within specified ranges. This perspective facilitates a more realistic representation of fluctuations in waste production, thereby enhancing both the robustness and predictive accuracy of our model. A case study conducted in Chaoyang District illustrates that, despite a declining population, total household waste continues to rise, highlighting the complexity and variability inherent in effective waste management practices.  
In summary, this research not only contributes to optimizing Beijing's municipal solid waste (MSW) management system but also provides valuable insights for other developed cities by offering innovative strategies for site selection and route optimization. The structure of this paper is organized as follows: the second section reviews the relevant literature; the third section details the methodologies employed along with data sources; the fourth section presents research findings.