Designing a cost-effective sensor placement plan for sewage surveillance is a crucial task because it allows cost-effective early pandemic outbreak detection as supplementation for individual testing. However, this problem is computationally challenging to solve, especially for massive sewage networks having complicated topologies. In this paper, we formulate this problem as a multi-objective optimization problem to consider the conflicting objectives and put forward a novel evolutionary greedy algorithm (EG) to enable efficient and effective optimization for large-scale directed networks. The proposed model is evaluated on both small-scale synthetic networks and a large-scale, real-world sewage network in Hong Kong. The experiments on small-scale synthetic networks demonstrate a consistent efficiency improvement with reasonable optimization performance and the real-world application shows that our method is effective in generating optimal sensor placement plans to guide policy-making.

翻译：设计经济高效的污水监测传感器布置方案是一项关键任务，因为它能够以低成本实现疫情爆发的早期检测，作为个体检测的补充手段。然而，该问题在计算上具有挑战性，特别是对于具有复杂拓扑结构的大规模污水管网。本文将这一问题建模为多目标优化问题以兼顾相互冲突的目标，并提出一种新颖的进化贪心算法（EG），以实现对大规模有向网络的高效优化。所提模型在小型合成网络和香港大规模真实污水管网中均进行了评估。在小型合成网络上的实验表明，该方法在保持合理优化性能的同时持续提升了效率；实际应用案例则证明，我们的方法能够有效生成最优传感器布置方案，为政策制定提供指导。