Snowplough service area reconfiguration using workload balancing techniques with route optimisation for large municipalities
研究了三种聚类方法重新配置加拿大萨里市的扫雪路线，结合改进的路线优化算法，在平衡工作量的同时节省了51分钟的模拟行驶时间。
Snowplowing is a complex, expensive, and mandatory winter fleet operation that benefits municipalities worldwide. In this research, three clustering approaches were used to create new snowplough route configurations for the City of Surrey, Canada, and the Smart Selective Navigator (SSN) method was used to optimise the routes. The three clustering approaches used are the current configuration-based dynamic clustering, static and dynamic clustering, and static and dynamic clustering with depot-to-cluster distance. The first clustering approach uses the existing configuration as a start point and makes minor changes, while the others generate new clusters from scratch with an objective of improving the workload distribution. SSN is a turn-based route optimisation algorithm that was improved by adding advanced turn-tracking methods capable of generating feasible routes in complex geographic information system (GIS) road network data. The simulation results show improvements when high-priority roads are clustered using the minor modification approach, and lower-priority roads are clustered from scratch. Overall, the clustering approaches can save 51 min of simulated travel time while significantly improving the workload balance.