Computational modeling demonstrates an effective multi-objective framework for freight train rescheduling, highlighting improved railroad operational resilience during unexpected disruptions.
Railroad daily operations can be significantly disrupted by unforeseen events such as adverse weather conditions and train accidents. These events often result in delays in delivering shipments on time. Therefore, studying how to reschedule freight trains during emergencies is essential for enhancing the competitiveness of rail transportation products. This paper proposed a method specifically designed for the rescheduling of freight trains, aiming to simultaneously modify the train operation plan and dynamically adjust railcar flow routes in response to unexpected events. The problem was formulated with a multi-objective model based on a two-layer space–time network. To accurately measure schedule deviations, we introduced a set of indicators specifically tailored to the characteristics of railroad freight transportation. The ε-constraint method was employed to establish a framework for searching three-dimensional Pareto solutions. Computational experiments were performed on a Chinese railroad network. The results validated the effectiveness and applicability of the proposed methodology.
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Bowen Ma (2026) studied this question.
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