Research on the Location Selection Problem of Urban Garbage Stations Based on the Epsilon Constraint Method

Abstract

This paper takes Zhenjiang City as an example and proposes a garbage station location framework that integrates spatial clustering and multi-objective optimization to address issues such as the imbalance in waste treatment facility layout and insufficient terminal treatment capacity in small and medium-sized cities. Firstly, a weighted K-Means clustering model is applied to spatially aggregate garbage generation points, identify high-demand areas, and reduce the problem scale. Secondly, a multi-objective integer programming model is constructed, which comprehensively considers key indicators such as transportation costs, operational costs, environmental impacts, and coverage rates. Pareto frontier analysis is used to reveal the trade-offs among different objectives. Finally, by incorporating practical constraints such as avoiding sensitive areas and capacity limitations, a location plan that balances economic efficiency and sustainability is generated. The research results demonstrate that this method can effectively balance costs and environmental impacts, providing a data-driven decision-making approach for garbage station planning in Zhenjiang City and other small and medium-sized cities.