Robust Tactical Crew Scheduling Under Uncertain Demand
研究了货运铁路在不确定需求下，如何提前几周制定乘务员排班计划，使计划在需求变化后仍能有效调整，减少司机能力与需求的不匹配。
We analyze a tactical freight railway crew scheduling problem, when train drivers must be informed several weeks before operations about the start and end times and locations of their duties. Between informing the train drivers and start of operations, trip demand changes due to cancellations, new bookings, and reroutings of trains, which might result in mismatches between train driver capacity at a location and demand. We analyze an approach that incorporates uncertain trip demand as scenarios, such that the start and end times and locations of the duties of a crew schedule are recoverable robust against deviations in trip demand. We develop a column generation solution method that dynamically aggregates trips to duties and decomposes the subproblems into smaller, computationally tractable instances. Our model determines duty frames that cover duties in many scenarios, creating recoverable robust crew schedules. We test our model on three real data sets of a major European freight railway operator. Our results show that our schedules are considerably more recoverable robust than those of the nominal solution, resulting in smaller mismatches between train driver capacity and demand.