卡车运输服务网络中动态装载计划的基于优化的学习
Optimization-based Learning for Dynamic Load Planning in Trucking Service Networks
AI总结：
本文研究出站装载计划问题（OLPP），将其表述为混合整数规划模型，提出字典序优化方法消除对称性，并设计结合机器学习与修复程序的优化代理，实现快速生成一致近优解，显著提升装载整合效率并节约成本。
摘要：
装载计划问题是包裹承运商服务网络设计中的一个关键挑战：它决定了在一段时间内，在成对的站点之间分配多少辆拖车进行调度。另一个关键挑战是确定一个流量计划，该计划指定如何将包裹量分配给已计划的装载。本文考虑了出站装载计划问题（Outbound Load Planning Problem, OLPP），该问题联合考虑了流量和装载计划挑战，以便在运营日之前，当站点需求预测随时间变化时，调整装载和流量。本文旨在开发一种决策支持工具，以协助规划者在网络各站点做出这些决策。本文将OLPP表述为一个混合整数规划模型，并表明在每种商品可通过主站点和备用站点进行路由的网络中，该模型存在大量对称性。结果，优化求解器可能会为密切相关的问题返回根本不同的解决方案，这会使规划者感到困惑并降低对优化的信任。为了弥补这一局限性，本文提出了一种字典序优化方法，该方法通过生成接近参考计划的最优解来消除这些对称性。此外，本文设计了一个优化代理，以解决优化模型的计算挑战。该优化代理结合了机器学习模型和修复程序，以找到满足规划者在环所施加的实时约束的近优解。对工业实例的广泛计算研究表明，该优化代理在生成相互一致的解决方案方面速度快了几个数量级。所提出的方法还展示了OLPP在装载整合方面的优势，以及结合机器学习和优化所获得的显著节约。
英文摘要：
The load planning problem is a critical challenge in service network design for parcel carriers: it decides how many trailers to assign for dispatch over time between pairs of terminals. Another key challenge is to determine a flow plan, which specifies how parcel volumes are assigned to planned loads. This paper considers the Outbound Load Planning Problem (OLPP) that considers flow and load planning challenges jointly in order to adjust loads and flows as the demand forecast changes over time before the day of operations in a terminal. The paper aims at developing a decision-support tool to inform planners making these decisions at terminals across the network. The paper formulates the OLPP as a mixed-integer programming model and shows that it admits a large number of symmetries in a network where each commodity can be routed through primary and alternate terminals. As a result, an optimization solver may return fundamentally different solutions to closely related problems, confusing planners and reducing trust in optimization. To remedy this limitation, this paper proposes a lexicographical optimization approach that eliminates those symmetries by generating optimal solutions staying close to a reference plan. Moreover, this paper designs an optimization proxy that addresses the computational challenges of the optimization model. The optimization proxy combines a machine-learning model and a repair procedure to find near-optimal solutions that satisfy real-time constraints imposed by planners in the loop. An extensive computational study on industrial instances shows that the optimization proxy is orders of magnitude faster for generating solutions that are consistent with each other. The proposed approach also demonstrates the benefits of the OLPP for load consolidation and the significant savings obtained from combining machine learning and optimization.
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