Browsing by Subject "Data-driven workforce optimization"

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    Optimal Workforce Planning and Shift Scheduling for Snow and Ice Removal
    (Minnesota Department of Transportation Research Services Section, 2010-12) Gupta, Diwakar; Tokar-Erdemir, Elif; Kuchera, Dustin; Mannava, Arun Kumar; Xiong, Wei
    Shrinking budgets and high equipment, fuel, and labor costs have raised the importance of workforce planning and efficient deployment of available workforce for county-level winter maintenance operations. This project focused on developing methodologies for the estimation workforce requirements, and economic evaluation of the impact of using contract employees, split shifts and staggered shifts. In order to achieve these goals, a fundamental question that needed to be addressed was the determination of the amount of work induced by different types of storms that occur in Saint Louis County. Researchers obtained relevant storm data from a variety of weather reporting sources and extracted parameters relevant for determining plow speeds and sand/salt consumption. These parameters were used to determine optimal workforce deployment strategies that balance overtime and delay costs, which in turn provided estimates of the amount of plowing time needed for the goal of clearing roads within 24 hours after the end of snow fall. Plowing time calculations were subject to rules concerning when call outs can occur during off-shift hours. Plow time estimates were subsequently used to develop efficient algorithms to calculate workforce requirements.