Resource Allocation in an Uncertain Environment: Application to Snowplowing Operations in Utah

We consider a two-stage planning problem where a fleet of snowplow trucks is divided among a set of independent regions, each of which then designs routes for efficient snow removal. The central authority wishes to allocate trucks to improve service quality across the regions. Stochasticity is introduced by uncertain weather conditions and unforeseen failures of snowplow trucks. We study two versions of this problem. The first aims to minimize the maximum turnaround time (across all regions) that can be sustained with a user-specified probability. The second seeks to minimize the total expected workload that has not been completed within a user-specified time frame. We develop algorithms that solve these problems effectively and demonstrate their practical value through a case application to snowplowing operations in Utah, obtaining solutions that significantly outperform the allocation currently used in practice.

Funding: Financial support from the Utah Department of Transportation [Grant 218138]; the National Science Foundation [Grant CMMI-2112758]; and the Mountain-Plains Consortium [Grant 637] is gratefully acknowledged.

Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.0024.