Linear-Time Optimal Deadlock Detection for Efficient Scheduling in Multi-Track Railway Networks
Track: Highlight
Categories: Transportation, Reliability of Critical Systems, Simulations/Datasets for applications in Critical Infrastructure, AI for detection and/or repair of service interruption
Keywords: Linear-Time, Deadlock Detection, Railway Scheduling
Abstract: The railway scheduling problem requires the computation of an operable timetable that satisfies constraints involving railway infrastructure and resource occupancy times, while minimising average delay over a set of events. Since this problem is computationally hard, practical solutions typically roll out feasible (but suboptimal) schedules one step at a time, by choosing which train to move next in every step. The choices made by such algorithms are necessarily myopic, and incur the risk of driving the system to a deadlock.
To escape deadlocks, the predominant approach is to stay away from states flagged as potentially unsafe by some fast-to-compute rule R. While many choices of R guarantee deadlock avoidance, they are suboptimal in the sense of also flagging some safe states as unsafe. In this paper, we revisit the literature on process scheduling and describe a rule R_0 that is (i) necessary and sufficient for deadlock detection when the network has at least two tracks in each resource (station / track section), (ii) computable in linear time, and (iii) yields lower delays when combined with existing scheduling algorithms on both synthetic and real data sets from Indian Railways.
Submission Number: 19
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