基于电商平台干线-支线协同决策优化需求，探讨一种多商品供需未匹配两阶段车辆路径问题。建立一个混合整数规划模型，基于成本节约思想构建初始可行解，并通过3个移除算子、1个修复算子、2个插入算子、4个交换算子和迭代局部搜索框架来改善初始解质量。研究表明：①该算法可快速对小规模问题求出最优解，求解中大规模问题在质量和效率上明显优于CPLEX；②提出的协同决策方案相对企业人工决策方案可节约16.65%的成本；③多商品联合运输相对单商品独立运输可节约69.83%的成本；④软时间窗相对硬时间窗可节约4.92%的成本。

To address the demand for the collaborative-decision optimization between linehaul and delivery transport of e-commerce platforms, this study investigates a novel variant of two-echelon vehicle routing problem by jointly considering three important practical factors: multi-commodity, unpaired supply-demand and soft time windows. This study formulates a mixed-integer programming model, and constructs a simple heuristic based on the cost-saving principle to quickly obtain an feasible initial solution, which is improved by incorporating three removal operators and one repair operator, two insertion operators and four exchange operators into an iteral local search framework. The experimental results show that: ① Our iterative local search algorithm can quickly obtain the optimal solutions for small scale problems, and has obvious advantages over CPLEX in solution quality and efficiency for medium and large scale problems; ② Compared with the manual decision of enterprises practice, our collaborative decision can save 16.65% of costs; ③ Compared with single-commodity transportation pattern, multi-commodity combination transportation can save 69.83% of costs; ④ The relaxation of hard time windows constraints can save 4.92% of costs.