摘要
车辆与无人机联合配送模式在产业界受到青睐,该模式有效地降低了配送成本,但却有极大的调度难度,问题的求解也非常复杂。本文对问题进行明确定义并建立模型,根据问题特性设计了一个自适应大规模邻域搜索(Adaptive Large Neighborhood Search,ALNS)算法,进行了大量的实验的对比和分析。研究结果表明,ALNS算法相比Gurobi在运行时间上有明显优势,结果相同甚至更优;车辆与无人机联合配送模式也较仅卡车配送模式节约了成本。
The joint distribution mode of vehicle and drone is popular in the industry.This mode effectively reduces the distribution cost,but it has great scheduling difficulty,and the solution of the problem is also very complex.In this paper,the problem is clearly defined and the model is established.According to the characteristics of the problem,an adaptive large neighborhood search(ALNS)algorithm is designed.The results show that,compared with Gurobi,ALNS algorithm has obvious advantages in running time,and the results are the same or even better;the joint distribution mode of vehicle and drone also saves the cost compared with the only truck distribution mode.
作者
王新
王征
徐伟
WANG Xin;WANG Zheng;XU Wei(School of Transportation Engineering,Dalian Maritime University,Dalian 116026,China;School of Maritime Economics and Management,Dalian Maritime University,Dalian 116026,China)
出处
《运筹与管理》
CSSCI
CSCD
北大核心
2021年第5期31-37,共7页
Operations Research and Management Science
基金
国家自然科学基金项目(71971036,71971037,71571027,71531002)
教育部人文社科一般项目(19YJA630084)
大连市重点学科重大课题研究项目(2019J11CY002)
辽宁省科技厅重点研发计划项目(2020JH2/10100042)。
