目的地拼箱与路线规划协同优化：多式联运约束下的多目标优化模型

Collaborative Optimization of Destination-Based Consolidation and Route Planning: A Multi-Objective Optimization Model under the Constraint of Multimodal Transport

摘要: 面向中美跨境链路的“最后一公里”，本文围绕目的地拼箱(DBC)–路线规划(VRPTW)协同优化这一研究缺口，提出在多式联运约束下的双目标混合整数线性规划(MILP)。模型将起运端的目的地拼箱决策与落地端的车辆路径–时间窗决策联动，并将铁路直达/小港泊船可用性、班期与堆场时窗等基础设施约束内生化；以总时长与总成本为双目标，兼顾服务水平。求解上，采用ε-约束标量化 + 分枝割(branch-and-cut)为主过程，辅以聚类–拼箱启发式暖启动与Benders样式分解加速。基于企业多源运营数据与可复现实例，开展拥堵(κ)/政策查验时延(δ)/运能松紧(φ)情景分析与稳健性检验。结果显示，所提一体化策略在中高拥堵与订单碎片化情景下相对分段基线具有稳定优势，且对铁路直达与小港泊船的可用性更为敏感。本文贡献在于：① 提出能捕捉拼箱–路径协同效应的可计算模型；② 给出工程可用的求解流程与性能报告框架；③ 提供面向不同情景的策略启示与适用边界，为跨境末端网络的时效–成本统筹提供方法论支撑。

Abstract: Targeting the “last-mile” segment in China-US cross-border logistics, this study addresses the research gap on the collaborative optimization of destination-based consolidation (DBC) and vehicle routing with time windows (VRPTW). We formulate a bi-objective mixed-integer linear program (MILP) under multimodal constraints. The model links the destination-based consolidation decision at the origin end with the vehicle route-time window decision at the destination end, and internalizes infrastructure constraints such as rail direct/small port berthing availability, schedule and yard time window; it takes total time and total cost as dual objectives, while taking service level into account. To solve it, we adopt an ε-constraint scalarization coupled with a branch-and-cut framework, enhanced by a clustering–consolidation heuristic warm start and a Benders-style decomposition for acceleration. Using enterprise multi-source operational data and reproducible test instances, we conduct scenario analyses with port congestion (κ), inspection delay (δ), and capacity tightness (φ), together with robustness checks. The integrated approach consistently outperforms sequential baselines in medium-to-high congestion and fragmented-demand settings, and its gains are sensitive to the availability of rail-direct access and small-port transshipment. The contributions are threefold: 1) a computable model that captures the DBC-routing synergy; 2) providing a solution process and performance reporting framework that can be used in engineering; and 3) providing strategic insights and applicable boundaries for different scenarios, and providing methodological support for the timeliness-cost coordination of cross-border last-mile networks.