基于网络Voronoi图启发式和群智能的最大覆盖空间优化

提出一种基于网络Voronoi面域图的最大覆盖选址模型及相应的粒子群优化方法，并应用于城市响应时间敏感型公共服务设施的空间优化。本文考虑设施功能沿交通网络辐射以及需求非均匀分布情形，对设施在网络连续空间上进行布局优化，选址模型采用网络Voronoi面域图划分布局设施的功能辐射域，以启发空间优化最小化重叠覆盖。模型同时顾及了设施利用率的最大化，规定设施对给定距离以内的需求实行的完全服务覆盖和对给定距离以外的需求实行随距离衰减的部分服务覆盖。本研究提出基于遗传机制和广义Voronoi图改进的粒子群算法以提高其空间优化性能，通过对南京市消防站最大覆盖空间优化实验表明，该研究取得了较为理想的结果，可应用于城市化区域应急设施最大覆盖空间优化。

Maximal Covering Spatial Optimization Based on Network Voronoi Diagrams Heuristic and Swarm Intelligence

A maximal covering location model based on network Voronoi area diagrams and particle swam optimization is proposed to provide the spatial optimization means for response sensitive public service facilities in urbanized area.It is taken into account that facilities function conducts along traffic network and variable demands distribute continuously,the facilities optimized can be located in continuous network space.The network Voronoi area diagrams are used to simulate the service areas of facilities in the maximal covering location model,which has heuristic to minimize overlap coverage in spatial optimization.The proposed model maximizes utilization of facilities,the demands within coverage radius are covered completely and the demands beyond coverage radius are covered partially by facilities function.An improved particle swam optimization algorithm integrated with genetic mechanism and generalized Voronoi diagram is proposed to enhance the optimization performance in continuous network space.The computational experiment for location optimization of fire stations in Nanjing shows that the proposed model and optimization algorithm have achieved desired results.