电商物流仓储波次与配送路径的协同优化模型

Collaborative Optimization Model of E-Commerce Logistics Warehousing Waves and Distribution Paths

摘要: 当前，电商仓库的订单批处理环节普遍存在智能化水平不足的问题。多数企业仍依赖基于固定规则或简单经验的批处理策略，难以适应订单动态波动与实时变化的物流资源。且存在波次拣选仅优化中转配送仓库内部作业效率，却忽略下游到网点的车辆路径约束，导致系统协同失效：路径规划被动接受固化的订单分组，无法通过动态重组提升运输效率，直接导致了拣选路径重复、车辆装载率低、无效运输与空驶现象频发，进而引发资源协同失效、成本次优与动态响应迟滞问题，严重制约了物流效率的提升与运营成本的优化，已成为行业在激烈竞争中亟待突破的瓶颈。为此，在电商仓储背景下，首先分析了波次拣选优化与车辆路径规划之间的相互影响，提出了在波次优化中嵌入配送路径成本的协同决策策略。该策略旨在同时最小化跨库区作业量与波次配送路径成本，构建了以最小化仓储–配送系统总成本为目标的上下层协同优化模型，并采用Gurobi求解器对该模型进行求解。通过典型算例及对比协同优化模型与独立分段模型的结果表明，协同优化模型在多次计算下的总成本与计算时间均表现更优，证明了所建模型的有效性。

Abstract: Currently, a common issue in e-commerce warehouses is the insufficient level of intelligence in batch order processing. Most companies still rely on batch processing strategies based on fixed rules or simple experience, making it difficult to adapt to dynamic fluctuations in orders and real-time changes in logistics resources. Additionally, wave picking often optimizes only the internal efficiency of transfer and distribution warehouses, neglecting vehicle route constraints for downstream delivery to outlets. This leads to system coordination failure: route planning passively accepts fixed order groupings and cannot improve transportation efficiency through dynamic reorganization, directly resulting in repeated picking routes, low vehicle load rates, invalid transport, and frequent empty runs. Consequently, this causes resource coordination failure, suboptimal costs, and delayed dynamic response, severely limiting improvements in logistics efficiency and operational cost optimization. In response, in the context of e-commerce warehousing, this study first analyzes the interaction between wave picking optimization and vehicle routing planning, and proposes a collaborative decision-making strategy that embeds distribution route costs into wave optimization. This strategy aims to simultaneously minimize cross-warehouse operation volume and wave distribution route costs, constructing a hierarchical collaborative optimization model with the goal of minimizing the total cost of the warehouse-distribution system, and employs the Gurobi solver to solve this model. Analysis of typical examples and comparison of results between the collaborative optimization model and the independent segmented model show that the collaborative optimization model performs better in terms of total cost and computation time across multiple calculations, proving the effectiveness of the proposed model.