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arXiv · 2606.16814

An Efficient MaxSAT-DDD Approach for Train Rescheduling via Precedence Propagation and Hybrid AMO Encodings

Tuyen Van Kieu·Tan Huu Nguyen·Khanh Van To
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Abstract

Train rescheduling repairs disturbed timetables while enforcing train-path precedence, resource capacity, and delay objectives. Dynamic Discretization Discovery (DDD) avoids full time discretization by refining only time points needed to certify feasibility and optimality. We strengthen a recent MaxSAT-DDD model through two encoding changes. First, resource conflicts are encoded as time-dependent at-most-one cliques, using pairwise clauses for small cliques and a sequential counter for large cliques. Second, earliest feasible times are propagated along train paths before the first DDD iteration. We evaluate four MaxSAT variants, two SAT optimization backends, Gurobi/CPLEX MILP models, and CPLEX CP on 72 instances and three delay objectives. MaxSAT-DDD solves all stepwise instances in about 23 ms on average. MaxSAT-Default reduces rounded-cost runtime from 794 to 479 ms, and the ablation study reports up to 79.6\% runtime reduction on the common-solved subset of hard continuous track instances.

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Tuyen Van Kieu, Tan Huu Nguyen, Khanh Van To. 2026-06-15. An Efficient MaxSAT-DDD Approach for Train Rescheduling via Precedence Propagation and Hybrid AMO Encodings. https://arxiv.org/abs/2606.16814

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