Highlights • Introduces a novel iterative heuristic for clustering, allocation and routing of snow and ice control vehicles. • Heuristic addresses important real-world operational constraints. • Heuristic incorporates a continuous measure of link criticality within the allocation step. • The link-criticality measure is shown to improve the time it takes to service the most critical links in an application to the state of Vermont’s roadway network. • The application also shows that advanced vehicle-allocation methods can be more effective at improving snow and ice control service than adding trucks to the fleet.

Abstract Many states in the U.S. have experienced increased demand for roadway snow and ice control (RSIC) operations due to an increase in extreme winter weather. As the number and severity of extreme weather events increases, the costs associated with winter roadway maintenance materials, plow operator time, equipment maintenance and replacement, and fuel use will also increase. In this paper, we introduce a unique heuristic procedure which we combine with real-world operational constraints to advance both the modeling and practical application of RSIC operations by incorporating a continuous measure of priority into a sequenced, iterative heuristic for network clustering, vehicle allocation, and capacitated vehicle routing. The heuristic balances the competing objectives of minimizing the total vehicle hours traveled for the fleet and minimizing the total time required to service the most critical links in the roadway network, while ensuring that the entire fleet is put to use. We also introduce a new measure of route-system performance, which is based on an effective link length (adjusted for how critical the link is to the performance of the entire system) and the time it takes to service the most critical links. We demonstrate the approach in practice by running five different applications of the heuristic on the statewide roadway network in Vermont. We demonstrate conclusively that our heuristic is effective for servicing the most critical links in the network in the least amount of time. We show that our more advanced vehicle allocation methods result in more effective RSIC service operations than adding vehicles to the fleet.