Abstract Manager

Snow-clearing operations in urban areas can be modelled as a Capacitated Arc Routing Problem, where the objective is to determine the optimal traversal strategy for a fleet of snow removal vehicles across a street network, represented as a graph, while adhering to various operational constraints and capacity limitations. This study, commissioned by the Town of Aurora in Ontario, develops an algorithm to support tactical-level decision-making in snow-clearing operations. The tactical decisions include fleet sizing, route optimization, task assignment, and operation type determination. Specifically, the algorithm determines the number and type of vehicles required for a given snowstorm scenario, identifies the most efficient traversal strategy for snow-clearing vehicles, allocates tasks between contractors and the Town’s municipal fleet, and classifies the type of snow-clearing operation—plowing, salting, patrolling, or sidewalk clearing—necessary for different segments of the street network. The algorithm accounts for operational constraints such as staff shifts, service level agreements, and time-to-clear benchmarks and is tested under various snowfall scenarios, ranging from moderate 5 cm events to extreme 100-year snowstorms. By prioritizing primary and secondary streets, the algorithm ensures compliance with service level requirements and provides the Town with the ability to respond effectively to snowstorm conditions.