In railway planning, effective disruption management tools can limit the impact of a disruption. A key challenge is that disruption durations are typically uncertain, leading to inefficiencies such as unnecessary shunting, seat-shortages, end-of-day inventory mismatches, and trip cancellations. To address this challenge, we propose two different approaches for incorporating robustness in real-time rolling stock rescheduling under uncertain disruption duration, an aspect that is typically assumed to be known in existing rescheduling models.The first approach, Light trip robustness (LTR) requires that a pre-specified percentage of critical trips are robust with respect to their incoming composition across different disruption durations. The second approach, Strict composition robustness (STR), ensures feasibility across all possible disruption durations without further changes to compositions or shunting operations. Both methods extend a well-known rolling stock rescheduling model by explicitly accounting for multiple possible disruption durations.We develop an online evaluation framework and conduct a computational study using real-world data from the Dutch railway network. Our results demonstrate that explicitly accounting for disruption duration uncertainty can significantly reduce rescheduling costs when disruptions last longer than initially expected. For instance, the STR model achieves up to 21% lower total costs compared to models that do not consider robustness, while the LTR model offers flexibility and performs well under moderate uncertainty. However, these benefits can come at a cost when the initially expected disruption duration is accurate. Overall, our results show that robust rolling stock rescheduling can significantly improve operational resilience and reduces the need for reactive updates. The appropriate level of robustness should be chosen based on the likelihood of disruption duration extensions.
The adoption of electric buses introduces challenges related to limited driving range, extended charging times, and strict battery management requirements. These requirements — often set by battery leasing companies or warranty providers — typically include recommended ranges for the battery’s state-of-charge (SoC). This study addresses these challenges by proposing a novel chance-constrained model for the electric vehicle scheduling problem that accounts for stochastic energy consumption and battery management guidelines. The model ensures compliance with recommended SoC ranges while optimizing operational costs. A tailored branch-and-price heuristic with stochastic pricing problems is developed. Computational experiments on realistic instances show that the stochastic approach offers good trade-offs between operational costs and compliance with battery management guidelines, reducing costs by up to 10% compared to a deterministic worst-case benchmark. In contrast, we show that relying on expected energy consumption yields solutions with a near-certain probability of violating SoC guidelines, rendering such an approach unsuitable for practical use.
The determination of preferences is a critical step of modelling strategic conflicts within the framework of Graph Model for Conflict Resolution (GMCR) and can significantly affect the results of equilibrium. In the existing GMCR methodologies, preferences are determined based on human judgement, affecting the credibility of equilibrium for guiding decision makers (DMs) to handle conflicts. In this paper, a novel approach is developed based on Natural Language Processing (NLP) to extract text from websites for automatically determining preferences. Algorithms are designed for the first time to convert online text into the preference statements in the option prioritization structure within the paradigm of GMCR. Three types of preference statements are constructed. This novel methodology is applied to air carbon negotiation, suggesting guidance of actions for DMs regarding how to be involved into the international carbon mitigation in aviation industry. Comparing with the existing preference modelling approaches within GMCR, our methodology can be utilized for determining preferences with more efficiency and credibility.
