目的 针对快递物流中客户交付模式多样化及临时变更所带来的路径复杂性问题,优化在动态交付模式下的快递配送路径,降低企业的配送成本,提高配送效率与服务响应能力。方法 构建融合时间窗约束的多车辆路径优化模型,设计无优先级、快递柜优先与送货上门优先3种策略,以配送成本和时间窗惩罚成本为目标函数;提出模拟退火改进元胞遗传算法（SACGA）进行求解,并对比3种策略在动态环境下的优化效果。结果 SACGA在精度与效率方面均优于SA、GA和CGA算法。在总成本上,策略3较策略1和策略2分别降低了37.4%和50%,路径结构最优,访问快递柜次数与路径长度最少,显著提升配送效率。结论 所构建模型与算法能有效应对交付模式临时变更问题,策略3结合SACGA在动态环境下展现最优性能,具备较强现实适用性与推广价值。
Abstract
The work aims to address the issue of path complexity caused by the diversification and temporary changes in customer delivery modes in express logistics, optimize the express delivery routes under the dynamic delivery mode, reduce the delivery costs of enterprises, and improve delivery efficiency and service response capabilities. A multi-vehicle path optimization model incorporating time window constraints was constructed. Three strategies were designed: no priority, express locker priority, and door-to-door delivery priority, with delivery cost and time window penalty cost as the objective functions. The simulated annealing-enhanced cellular genetic algorithm (SACGA) was proposed for a solution, and the optimization effects of the three strategies in a dynamic environment were compared. SACGA outperformed SA, GA, and CGA algorithms in both accuracy and efficiency. In terms of total cost, Strategy 3 reduced costs by 37.4% and 50% compared with Strategies 1 and 2, respectively, with the optimal path structure, the fewest visits to parcel lockers, and the shortest path length, significantly improving delivery efficiency. It is evident that the constructed model and algorithm can effectively address the issue of temporary changes in delivery modes. Strategy 3 combined with SACGA demonstrates optimal performance in dynamic environments, demonstrating strong practical applicability and promotional value.
关键词
路径优化 /
交付模式 /
模拟退火改进元胞遗传算法 /
时间窗
Key words
path optimization /
delivery mode /
SACGA algorithm /
time window
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基金
国家自然科学基金（71971130）; 山东省重点研发计划（软科学）项目（2021RZB02007）
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