# A Dynamic Framework for Community-Facility Siting with Inter-Community Competition
## 6 Discussion  
### The Effectiveness of the BOMSDM  
By comparing the BOMSDM with the realistic solution and traditional models proposed in the previous literature and conducting a sensitivity analysis of facility redundancy on the BOMSDM's results, based on the research findings, we derive the following three key conclusions:  
First, the empirical study demonstrates that the BOMSDM is effective because, compared to the realistic solution, the proposed BOMSDM significantly enhances the efficiency of community facilities' service provision and promotes social equity in service distribution while maintaining SDGC within the optimal fairness range. This study reveals that in Shaoxing City, the existing distribution of recycling stations shows low service efficiency, likely due to independent facility placement by community committees, causing overlapping service areas between neighboring communities, or poor accessibility resulting from decision-making errors, which highlights the necessity of a global optimization model for facility location. The BOMSDM addresses these issues by considering both individual facility constraints and overall service efficiency, offering decision-makers an effective tool for global optimization.  
Second, in the empirical study on the site selection of recycling stations in Shaoxing, we found that BOMSDM is superior because, compared to traditional models with random facility-community allocation strategies, the BOMSDM may slightly sacrifice global service efficiency and fairness but substantially improve the balance in the allocation of community facilities among communities. Previous location optimization models, such as those by  
Figure 6: The impact of facility redundancy.  
[PERSON] et al. [53], [PERSON] and [PERSON] [47], [PERSON] et al. [57], and [PERSON] et al. [58], provide effective global frameworks for optimizing facility efficiency and equity. These models focus on placing facilities in high-demand areas, such as population centers, to maximize accessibility, coverage, and supply. However, in practice, this can lead to a different form of inequity and waste, as concentrating facilities in dense urban areas may lead to redundancy, while remote or peripheral communities are excluded. Unlike traditional models, the BOMSDM innovatively prioritizes equity in facility allocation, ensuring service delivery to marginalized communities.  
Third, through the sensitivity testing of facility redundancy in the empirical study, we found that BOMSDM achieves optimal optimization capability when there is a certain level of facility redundancy. In this research, when the facility redundancy reached approximately 20%, the target values obtained by the BOMSDM closely aligned with those from Model II, which employs a random strategy. This finding indicates that, in this instance, the BOMSDM can effectively offset the target value losses incurred from earlier facility-community allocation constraints through the strategic addition of a limited number of redundant facilities. This underscores the superiority of the BOMSDM's scenario-based dynamic strategy. Additionally, it offers valuable insights for decision-makers, highlighting the necessity of incorporating a certain degree of redundancy beyond the existing 1:1 facility-community allocation strategy to enhance operational efficiency.