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

Yinhu Wang (), Ye Chen (), Ilya O. Ryzhov (), Xiaoyue Cathy Liu () and Nikola Marković ()
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Yinhu Wang: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112
Ye Chen: Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, Virginia 23284
Ilya O. Ryzhov: Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742
Xiaoyue Cathy Liu: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112
Nikola Marković: Department of Civil and Environmental Engineering, University of Utah, Salt Lake City, Utah 84112

Transportation Science, 2024, vol. 58, issue 4, 781-800

Abstract: 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.

Keywords: resource allocation; stochastic optimization; arc routing; snow plowing; fleet management (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:58:y:2024:i:4:p:781-800

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