本文研究了O2O转型背景下大型零售企业城市配送网络优化面临的中转中心选址及末端需求点分配问题，构建了考虑配送中心到末端需求点近似配送距离的中转中心选址及末端需求点分配联合优化模型，设计了集成遗传算法和禁忌搜索算法的混合算法求解模型，通过混合算法与CPLEX的对比证明了本文算法的有效性。以苏宁重庆主城核心区域的城市配送网络优化为实例进行测算，给出了苏宁重庆主城核心区域中转中心的选址及需求点分配的联合优化方案。联合优化方案表明城市物流"最后一公里"末端配送成本占城市物流总成本的比例超过60%；车型是影响城市物流总成本的关键因素，选择与中转中心容量接近的车型能够显著降低城市物流总成本，减少运输距离；合理的中转中心选择能提高短距离配送比例和减少配送总距离。
In this article, joint location-assignment problem is studied for designing an Urban Logistics Network (ULN) with multiple intermediate depots (IDs) and terminals of ULN. The key decisions are where to locate the intermediate depots (IDs) and how to assign terminals to IDs such that the total network cost is minimized. A large-scale static and deterministic integer programming model is presented solving a joint location-assignment problem of large chain retailers. To solve this model, a more efficient hybrid algorithm integrated with Genetic algorithm and Tabu search algorithm is put forward based on heuristic searching criteria. Our methodology is illustrated with the Urban Logistics Network from a leading Chinese retailer (Suning) in Chongqing. Numerical analysis suggests that optimal solutions can reduce the total network cost between 2.92%~14.36% by comparison with the current ULN.Delivery costs of "Last Mile" account for total urban logistics network costs more than 60%. Vehicle type is a deterministic factor to total urban logistics network costs. The effects of changing parameter values on the optimal solutions are also studied and some management implications are pointed out.
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