B2B配送中考虑时间窗一致性的车辆路径优化模型与算法
Model and algorithm for vehicle routing problem considering time window consistency in B2B distribution
姚宇 1莫鹏里 2商攀 3郑长江 1朱晓宁3
作者信息
- 1. 河海大学土木与交通学院,南京 210095
- 2. 东南大学交通学院,南京 211189
- 3. 北京交通大学交通运输学院,北京 100044
- 折叠
摘要
在B2B(business to business)物流配送中,收货方通常对配送时间的规律性有较高要求,以配合自身的生产经营活动.对考虑时间窗一致性的车辆路径优化问题展开研究,构建其混合整数线性规划模型,并设计自适应大规模邻域搜索算法进行求解.针对每日配送路径在时间维度的一致性耦合关系,提出距离优先和时间窗优先相结合的优化策略,在算法框架中嵌入时间窗标定及一致性检验模型,并设计联动型算子以对每日路径方案进行协同操作.基于既有数据集、自建数据集和大规模实际算例对模型算法的有效性进行验证,结果表明,所提算法可以快速求得高质量解,提出的时间窗优先策略及对应的联动型算子可以显著提升算法性能.根据数值计算结果量化分析实现时间窗一致性的附加运输成本,揭示了不同客户点规模和时间窗长度下一致性成本的变化规律.
Abstract
In B2B(Business to Business)distribution,the customer usually has high requirements on the regularity of delivery time to accommodate its production and operation activities.This study investigates the vehicle routing problem considering time window consistency,constructs its mixed integer linear programming model,and develops an adaptive large neighborhood search algorithm to solve it.To address the consistency coupling relationship between daily routes in the time dimension,this study proposes an optimization strategy that combines distance-first and time-window-first techniques,where time window assignment and consistency check models are embedded,and the interdependent operators are designed to optimize the routes on different days collaboratively.The proposed model and algorithm are tested based on the existing dataset,self-built dataset,and a large-scale real-world case.The results show that the proposed algorithm can obtain high-quality solutions efficiently,and the proposed time-window-first technique and the corresponding interdependent operators can significantly improve the performance of the algorithm.Finally,the additional transportation cost of achieving time window consistency is quantified and analyzed,and the variation of consistency costs under different customer sizes and time window lengths is revealed.
关键词
物流工程/车辆路径问题/B2B配送/一致性/自适应大规模邻域搜索Key words
logistics engineering/vehicle routing problem/B2B distribution/consistency/adaptive large neighborhood search引用本文复制引用
基金项目
国家自然科学基金联合基金项目(U2034208)
中央高校基本科研业务费专项资金项目(B220201022)
出版年
2024
NETL
NSTL
维普
万方数据