A data-driven approach to manpower planning at U.S.–Canada border crossings |
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| Institution: | 1. Department of Civil and Environmental Engineering, The University of California-Berkeley, Berkeley, CA 94720, USA;2. Sauder School of Business, University of British Columbia, Vancouver, V6T 1Z2 British Columbia, Canada;3. Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA;1. School of Mechanical Engineering, Jiangsu University, Zhenjiang 212013, China;2. Faculty of Science, Jiangsu University, Zhenjiang 212013, China;1. Department of Industrial and Systems Engineering, 96 Frelinhuysen Road, CoRE Building 212, Piscataway, NJ 08854, United States;2. Department of Energy and Resources Engineering, Peking University, Beijing 100871, China |
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| Abstract: | We investigate the staffing problem at Peace Arch, one of the major U.S.–Canada border crossings, with the goal of reducing time delay without compromising the effectiveness of security screening. Our data analytics show how the arrival rates of vehicles vary by time of day and day of week, and that the service rate per booth varies considerably by the time of day and the number of active booths. We propose a time-varying queueing model to capture these dynamics and use empirical data to estimate the model parameters using a multiple linear regression. We then formulate the staffing task as an integer programming problem and derive a near-optimal workforce schedule. Simulations reveal that our proposed workforce policy improves on the existing schedule by about 18% in terms of average delay without increasing the total work hours of the border staff. |
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| Keywords: | Data-driven Workforce policy Queueing Empirical data analysis |
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