Call Window Scheduling for Freight Rail Engineers

2026-08-02

AI Summary

This study addresses fatigue and safety risks faced by freight rail engineers due to on-call scheduling and uncertain transportation demand. To enhance schedule flexibility and predictability of rest periods while complying with Hours of Service regulations, the authors propose a call-window-based crew assignment mechanism as an alternative to traditional fixed assignments. They formalize this scheduling problem for the first time and develop a set-covering optimization model alongside a constructive heuristic algorithm, incorporating constraints such as deadhead trips, delay tolerance, and crew availability limits. Computational experiments on two- and three-city instances demonstrate that the optimization model achieves demand coverage rates of 94.89% and 91.09%, respectively—significantly outperforming the heuristic—while also yielding superior rest allocations and lower delays.

Abstract

This study investigates a new approach for scheduling freight rail engineers based on call windows under the Hours of Service (HOS) regulations. Unlike planned trip assignments, call windows specify a time interval during which an engineer may be required to start work, thus providing greater flexibility to handle uncertain trip demand while offering drivers more predictable off-duty periods. Currently in the U.S., all major freight operators require drivers to be available 24/7 outside of mandatory rest periods, raising concerns over workforce fatigue and safety. We formalize the call-window scheduling problem and propose two solution approaches: a Set-covering-type optimization model and a Direct Algorithm. Both aim to maximize demand coverage while minimizing the number of engineers subject to HOS feasibility. Computational experiments for 2-city and 3-city instances with varying call window lengths, maximum delay allowances, and whether to allow deadhead trips show that the Set-covering-type model yields higher demand undercoverage (94.89% and 91.09% for 2-city and 3-city instances, respectively) than the Direct Algorithm (93.96% and 71.83%). It also offers greater rest opportunities and reduced delays. Sensitivity analysis reveals that the upper limit on engineer availability significantly affects all key performance metrics.

Problem

• Call Window Scheduling
• Freight Rail Engineers
• Hours of Service
• Workforce Fatigue
• Demand Uncertainty

Innovation

• call window scheduling
• freight rail engineers
• Hours of Service (HOS)
• set-covering optimization
• demand uncertainty