Improving Snowplowing Operations in Utah Through Optimization and Visualization

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

Interfaces, 2024, vol. 54, issue 6, 471-486

Abstract: This paper describes efforts to improve snowplowing routes in 12 regions in northern Utah. Both exact and approximate methods are applied to determine snowplowing routes that decrease total travel time, turnaround time, and deadhead miles by an average of 5.04%, 15.01%, and 14.84%, respectively, across the 12 regions, which can significantly improve the efficiency of snow removal operations as well as the social welfare. Our models also evaluate the tradeoffs between different operational policies such as echelon versus nonechelon routing and centralized versus decentralized optimization and analyze various scenarios to understand the benefits of providing additional resources to different regions. These analyses enable local management teams to determine the best settings for their regions. In addition to optimization modeling, a major component of this work is the use of data visualization to demonstrate the effectiveness of the new routes (with comparisons to current practice) to the Utah Department of Transportation.

Keywords: snowplow routing problem; optimization; data visualization; field implementation; case studies (search for similar items in EconPapers)
Date: 2024
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