Location and capacity optimization of urban sanitation robot base stations using improved NSGA-II

Traditional sanitation systems are increasingly challenged by contemporary waste collection demands, owing to limited service capacity, high operational costs, and uneven resource distribution. Sanitation robots, capable of high-precision waste identification and multi-robot collaboration, offer a promising alternative to improve collection efficiency. Their performance, however, critically depends on the scientific layout of base stations. This study proposes a multi-objective location and capacity optimization model based on an improved NSGA-II, incorporating multi-source data such as population density and spatiotemporal waste distribution. To strengthen the algorithm's adaptability to complex urban environments, a self-adaptive crossover operator and a dynamic objective weighting mechanism are introduced. The TOPSIS method is then applied to evaluate and rank Pareto-optimal solutions, yielding an optimal layout and capacity plan for base stations. An empirical analysis in Lixia District, Jinan City, demonstrates that the optimized scheme reduces the number of facilities by one, improves service utility, and decreases the number of uncovered demand points to eight. Compared with existing planning approaches, the proposed model proves effective in dynamic scenarios and provides a collaborative multi-objective optimization framework for smart urban sanitation planning.