交通运输系统工程与信息

交通运输系统工程与信息 ›› 2026, Vol. 26 ›› Issue (5): 56-67.DOI: 10.16097/j.cnki.1009-6744.2026.05.005

• 青年基金项目成果 • 上一篇    下一篇

时变投放需求下智能回收箱清运车辆时空路径优化模型与算法

姚宇*1 ，朱昌浩1 ，汪怡然2 ，郭孟欢1   

  1. 1. 河海大学，土木与交通学院，南京210024；2.南京邮电大学，现代邮政学院、智慧交通学院，南京210003
  • 收稿日期:2026-04-08 修回日期:2026-05-28 接受日期:2026-06-30 出版日期:2026-10-25 发布日期:2026-10-21
  • 作者简介:姚宇（1993—），女，甘肃兰州人，副教授，博士。
  • 基金资助:
    国家自然科学基金青年科学基金 (72301094)；江苏省自然科学基金 (BK20230964)。

Model and Algorithm for Vehicle Spatiotemporal Routing Optimization in Smart Recycling Bin Collection Under Time-Varying Disposal Demand

YAO Yu*1, ZHU Changhao1, WANG Yiran2, GUO Menghuan1   

  1. 1. College of Civil and Transportation Engineering, Hohai University, Nanjing 210024, China; 2. School of Modern Posts, School of Intelligent Transportation, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
  • Received:2026-04-08 Revised:2026-05-28 Accepted:2026-06-30 Online:2026-10-25 Published:2026-10-21
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China (72301094)；Natural Science Foundation of Jiangsu Province, China (BK20230964)。

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摘要： 本文面向智能回收箱清运场景，针对时变投放需求下智能回收箱占用量动态变化、单箱服务次数不固定，以及服务时刻与实际清运量和箱体占用量强耦合等问题特征，提出一种清运车辆时空路径优化方法。本文将每一智能回收箱扩展为多个候选清空服务事件，构建以运营成本、过程拒收惩罚和期末残余占用惩罚之和最小化为目标的细粒度离散时间混合整数线性规划模型，并根据问题结构，设计内嵌交替迭代策略的自适应大规模邻域搜索算法（ALNS），形成“候选服务事件生成及更新-ALNS路径搜索-时空路径生成及调整”迭代求解框架。通过小规模对比算例、敏感性分析和实际场景算例，验证模型与算法的有效性。结果表明：本文方法能够生成高质量智能回收箱清运方案，与精确解的平均相对偏差仅为0.59%；惩罚系数决定服务水平与运营成本之间的权衡，需求时空特征影响增加车辆数量与提升车辆容量两类资源配置方式的相对有效性。实际场景算例进一步验证了所提方法在真实智能回收网络中的适用性。

关键词: 物流工程, 时空路径优化, 混合整数线性规划, 智能回收箱, 自适应大规模邻域搜索

Abstract: Focusing on the problem of smart recycling bin collection, this paper proposes a vehicle spatiotemporal routing optimization method for the collection problem under time-varying disposal demand. The problem is characterized by the dynamic evolution of bin occupancy, the non-predetermined number of services for each bin, and the strong coupling among service timing, actual collected amount, and bin occupancy state. To address these features, each recycling bin is expanded into multiple candidate emptying service events, and a fine-grained discrete-time mixed-integer linear programming model is developed to minimize the sum of operating cost, in-process rejection penalty, and end-of-horizon residual occupancy penalty. Based on the problem structure, an adaptive large neighborhood search algorithm embedded with an alternating iterative strategy is designed, forming an iterative solution framework consisting of candidate service event generation and updating, route search, and spatiotemporal route generation and adjustment. The effectiveness of the model and algorithm is verified through small-scale comparative instances, sensitivity analysis, and a real-world case study. The results show that the proposed method can generate the high-quality smart recycling bin collection plans, with an average relative gap of only 0.59% from the exact solutions. The penalty coefficient determines the trade-off between service level and operating cost, while the spatiotemporal characteristics of demand affect the relative effectiveness of two resource allocation strategies, namely increasing the number and capacity of vehicles. The real-world case study further verifies the applicability of the proposed method in an actual smart recycling network.

Key words: logistics engineering, spatiotemporal routing optimization, mixed-integer linear programming, smart recycling bins; adaptive large neighborhood search

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