Optimizing the number and location of household waste collection sites by multi-maximal covering location model: An empirical study in Minamata City, Kumamoto Prefecture, Japan. (15th December 2022) - Zhuo, Qiannan; Yan, Wanglin
Abstract: Convenience has consistently been perceived as one of the crucial determinants of household recycling behavior in source separation systems studied over the last several decades. Although residents' accessibility to collection sites significantly affects household recycling behavior, allocating the collection sites for household waste is complex for policymakers. It requires balancing between residents' needs and the limited budgetary and human resources of local government and consideration of waste management policies. This article proposes the multi-maximal covering location model (MMCLM) to solve the waste collection site allocation problem based on residents' satisfaction while considering the capacity of municipal services. In this study, MMCLM was applied in Minamata City, Kumamoto Prefecture, Japan, a city with more than 25 years of successful experience in implementing household recycling. Six scenarios were developed to allocate recyclable waste collection sites. With 10 new sites, there was a more than 5% improvement in average access distance to drop off waste for all households. 30 new sites could effectively reduce the average access distance by 72.4% for 324 households who currently have to walk more than 10 min, and 8.0% of the average access distance for all residents. Low requirements for input datasets and flexible parameters adjustment make MMCLM more feasible in various contexts, especially for small to medium-sized cities or developing regions. Graphical abstract: Image 1 Highlights: Proposed an integer model for allocating garbage collection sites. Easy to balance residents' needs and budget constraints by multi-coverages. Low requirements for input datasets and more applicable for developing regions. Feasible to solve this type of problems by flexible variables adjustment. Original data collection and scenario design for verification.