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A learning-based robust optimization framework for synchromodal freight transportation under uncertainty
Transportation Research Part E: Logistics and Transportation Review ( IF 8.3 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.tre.2025.103967
Siyavash Filom, Saiedeh Razavi

Synchromodal freight transport is characterized by its inherent dynamicity, necessitating the need for optimal decision-making in the presence of uncertainties in the real world. However, most prior research has overlooked the complexities of uncertainty modeling, often relying on assumed probability distributions that may not accurately reflect real-world conditions. This study presents a learning-based robust optimization framework for synchromodal freight transportation to derive data-driven explainable decisions. The study proposes a predict-then-optimize framework, using a combination of the Bayesian Neural Network with uncertainty quantification and dynamic robust optimization modules to solve the shipment matching problem under the synchromodality concept. The integration of prediction and optimization modules is achieved through scenario-based adjustable uncertainty sets. Rather than generating a single optimal solution, this framework produces an optimal policy based on various scenarios, enabling decision-makers to evaluate trade-offs and make informed decisions. The framework is implemented for the Great Lakes region containing nine intermodal terminals using real-world data and the performance is evaluated under various scenarios. In addition, a preprocessing heuristic-based feasible path generation algorithm is developed that helps the framework to maintain linear solution time. Numerical experiments performed on large demand instances (up to 700 shipment requests) demonstrate that the upstream prediction module significantly impacts the downstream optimization module. This effect is primarily due to variations in road travel times across scenarios, which impact transshipment operations, storage, and delay costs.

中文翻译:


基于学习的鲁棒优化框架,用于不确定性下的同步货物运输



同步货运的特点是其固有的动态性,因此需要在现实世界中存在不确定性的情况下做出最佳决策。然而,大多数先前的研究都忽视了不确定性建模的复杂性,通常依赖于可能无法准确反映真实世界条件的假设概率分布。本研究为同步货运提出了一个基于学习的稳健优化框架,以得出数据驱动的可解释决策。该研究提出了一个预测后优化框架,将贝叶斯神经网络与不确定性量化和动态稳健优化模块相结合,以解决同步模态概念下的货运匹配问题。预测和优化模块的集成是通过基于场景的可调不确定性集实现的。该框架不是生成单一的最佳解决方案,而是根据各种场景生成最佳策略,使决策者能够评估权衡并做出明智的决策。该框架是使用真实数据为包含 9 个多式联运终端的五大湖地区实施的,并在各种情况下评估了性能。此外,还开发了一种基于预处理启发式的可行路径生成算法,可帮助框架保持线性求解时间。对大型需求实例(最多 700 个发货请求)执行的数值实验表明,上游预测模块会显著影响下游优化模块。这种影响主要是由于不同情景的道路行驶时间不同,这会影响转运操作、存储和延误成本。
更新日期:2025-01-18
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