安全与环境学报

铁路重载运输由于运量大、效率高等显著优势，在缓解运输压力、降低物流成本以及提升运输效益方面发挥着重要作用。在"双碳"目标背景下，将碳排放因素纳入重载列车开行方案以优化研究，对提升铁路运输系统整体能效和减少碳排放具有重要现实意义。研究以重载货物列车为对象，构建以碳排放成本最小化和车流组织时间费用最小化为目标的双目标优化模型，同时考虑车站通过能力、组合站作业能力及区间通过能力等约束条件进行分析。针对模型求解问题，采用一种改进遗传算法进行求解。研究以某重载铁路为研究对象进行算例验证，并在求解过程中引入权重系数协调双目标，对不同权重配置下的开行方案进行对比分析。此外，为了研究碳税税率对优化方案和开行方案的影响，进行灵敏度分析。结果显示：侧重时间效率的方案可降低15.3%的时间成本，但碳排放成本较高；侧重碳排放的方案比其他方案减少1.9%碳排放成本；均衡方案下的开行方案5 000 t单元式直达列车与10 000 t组合式列车开行比例为44∶41,这一配置体现了模型在双目标之间取得了良好平衡，即生成了兼顾效率与环保的可行开行方案。灵敏度分析显示：碳税税率的上调，驱动模型采用碳排放成本较低的运输方案，通过调整直达与组合列车比例优化列车开行结构，该策略以时间成本的适度增加为代价，使碳排放总量下降了0.58%,验证了模型在双目标优化中的适应性和灵活性。

Heavy-haul rail transportation plays a crucial role in alleviating transport pressure, reducing logistics costs, and enhancing transportation efficiency due to its significant advantages of large capacity and high efficiency. In the context of the "dual carbon" strategy, integrating carbon emission factors into the optimization of heavy-haul train operation plans is essential for improving the overall energy efficiency of the rail transport system and reducing carbon emissions. This study focuses on heavy-haul freight trains and constructs a dual-objective optimization model aimed at minimizing both carbon emission costs and time costs associated with train flow organization. Constraints such as station throughput capacity, combined station handling capacity, and sectional throughput capacity are considered simultaneously. An improved genetic algorithm is employed to solve the model. A case study on a specific heavy-haul railway line is conducted for validation. Weighting coefficients are introduced during the solution process to harmonize the dual objectives, and operational plans under different weighting configurations are compared and analyzed. Finally, a sensitivity analysis is performed to investigate the impact of carbon tax rates on the optimized operational plans. The results indicate that the carbon-emission-focused scheme reduces carbon emission costs by 1.9% compared to other schemes. The balanced scheme achieves equilibrium between the two optimization approaches, resulting in a 44:41 ratio of unit-type direct trains to combined 10 000-ton trains. This configuration demonstrates the model's effectiveness in balancing dual objectives, generating feasible operation plans that reconcile efficiency with environmental protection. Furthermore, the sensitivity analysis reveals that increasing the carbon tax rate beyond the balanced plan leads to higher carbon emission costs and time expenses. This prompts the model to optimize its train organization strategy, favoring modes with lower carbon emissions, which results in a 0.58% reduction in total carbon emissions at the expense of a moderate increase in transportation time. This outcome illustrates the model's flexibility in achieving a balance between the dual objectives.