This paper addresses the Service Network Design (SND) problem for a logistics service provider (LSP) operating in a multimodal freight transport network, considering uncertain travel times and limited truck fleet availability. A two-stage optimization approach is proposed, which combines metaheuristics, simulation and machine learning components. This solution framework integrates tactical decisions, such as transport request acceptance and capacity booking for scheduled services, with operational decisions, including dynamic truck allocation, routing, and re-planning in response to disruptions. A simulated annealing (SA) metaheuristic is employed to solve the tactical problem, supported by an adaptive surrogate model trained using a discrete-event simulation model that captures operational complexities and cascading effects of uncertain travel times. The performance of the proposed method is evaluated using benchmark instances. First, the SA is tested on a deterministic version of the problem and compared to state-of-the-art results, demonstrating it can improve the solution quality and significantly reduce the computational time. Then, the proposed SA is applied to the more complex stochastic problem. Compared to a benchmark algorithm that executes a full simulation for each solution evaluation, the learning-based SA generates high quality solutions while significantly reducing computational effort, achieving only a 5% difference in objective function value while cutting computation time by up to 20 times. These results demonstrate the strong performance of the proposed algorithm in solving complex versions of the SND. Moreover, they highlight the effectiveness of integrating diverse modeling and optimization techniques, and the potential of such approaches to efficiently address freight transport planning challenges.

翻译：本文研究了运营于多式联运货运网络中的物流服务提供商（LSP）所面临的服务网络设计（SND）问题，考虑了不确定的运输时间和有限的卡车车队可用性。提出了一种结合元启发式、仿真和机器学习组件的两阶段优化方法。该求解框架整合了战术决策（如运输请求接收和预定服务容量预订）与运营决策（包括动态卡车分配、路线规划及因应中断的重新规划）。采用模拟退火（SA）元启发式算法求解战术问题，并由基于离散事件仿真模型训练的自适应代理模型提供支持，该模型能够捕捉运营复杂性和不确定运输时间的级联效应。使用基准实例对所提方法的性能进行了评估。首先，在确定性版本问题上测试了SA算法，并与现有最优结果进行了比较，结果表明该算法能够提高解的质量并显著减少计算时间。随后，将所提SA算法应用于更复杂的随机问题。与对每个解评价执行完整仿真的基准算法相比，基于学习的SA算法在显著降低计算量的同时生成高质量解，目标函数值仅相差5%，而计算时间最多可缩短20倍。这些结果证明了所提算法在求解复杂SND问题版本时的强大性能。此外，它们还凸显了整合不同建模与优化技术的有效性，以及此类方法在有效应对货运运输规划挑战方面的潜力。