[1]李嫚嫚,雷海泷,赵博选.考虑众包员供给动态性的末端配送优化方法[J].郑州大学学报(工学版),2025,46(06):102-111.[doi:10.13705/j.issn.1671-6833.2025.06.001]
　LI Manman,LEI Hailong,ZHAO Boxuan.Optimization Method for Last Mile Delivery Considering Supply Dynamics of Crowdsourced Personnel[J].Journal of Zhengzhou University (Engineering Science),2025,46(06):102-111.[doi:10.13705/j.issn.1671-6833.2025.06.001]
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考虑众包员供给动态性的末端配送优化方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年06期
页码:
102-111
栏目:
出版日期:
2025-10-22

文章信息/Info

Title:
Optimization Method for Last Mile Delivery Considering Supply Dynamics of Crowdsourced Personnel
文章编号:
1671-6833(2025)06-0102-10
作者:
李嫚嫚;  雷海泷;  赵博选
长安大学 汽车学院,陕西 西安 710021
Author(s):
LI Manman;  LEI Hailong;  ZHAO Boxuan
School of Automobile, Chang’an University, Xi’an 710021, China
关键词:
物流工程;  末端配送;  众包模式;  众包供给动态性;  车辆路径;  自适应大邻域搜索算法
Keywords:
logistics engineering;  last mile delivery;  crowdsourced mode;  supply dynamics of crowdsourced personnel;  vehicle routing;  adaptive large neighborhood search algorithm
分类号:
U9TP29F252.1
DOI:
10.13705/j.issn.1671-6833.2025.06.001
文献标志码:
A
摘要:
考虑众包员供给动态性,针对自营配送员与众包员共存情景,提出了一种末端配送优化方法。以众包员报酬、客户指派方案以及多行程车辆路径为待优化决策,基于时空网络建立客户服务约束、载重约束、时间窗约束以及车辆-众包员时空协同约束,构建以最小化配送成本为目标的混合配送优化模型。根据问题特征改进最小成本插入法、设计10种破坏算子,并依据模拟退火算法思想接受差解改进自适应大邻域算法求解模型,获取末端配送方案。结果表明:对于100个客户的案例,改进自适应大邻域算法以平均48.5 s获得的配送成本比GUROBI耗时1 h的更低,平均降低了24%;与模拟退火相比,改进自适应邻域算法获得的配送成本也更低,最大降低了5.5%。协同自营配送员与众包员的混合配送模式比自营配送模式的配送成本低;混合配送模式适用于时间窗紧凑、车辆行驶成本高、就业机会少的末端配送场景;众包员供给动态性显著且随机地影响混合配送模式的配送成本。
Abstract:
Considering the supply dynamics of crowdsourced personnel, an optimization method was proposed for last-mile delivery in scenarios involving both self-operated personnel and crowdsourced personnel in this study. A hybrid delivery optimization model was developed to minimize delivery costs, incorporating decision variables such as crowdsourced personnel compensation, customer assignment schemes, and multi-trip vehicle routing. The constraints was constructed based on a spatio-temporal network, including customer service requirements, load capacities, time windows, and spatio-temporal coordination between vehicles and crowdsourced personnel. To address the problem′s characteristics, the minimal cost insertion algorithm was improved, and ten types of destruction operators were designed. An adaptive large neighborhood search algorithm was then improved by integrating the simulated annealing algorithm′s concept of accepting inferior solutions to optimize delivery plans.Case studies with 100 customers demonstrated that the improved adaptive large neighborhood search algorithm achieved solutions with 24% lower in delivery cost on average compared with those obtained by GUROBI with 1 hour computation time, while taking only 48.5 seconds on average. The proposed algorithm also outperformed simulated annealing, achieving a maximum cost reduction of 5.5%. The hybrid delivery mode combining self-operated and crowdsourced personnel significantly reduced costs compared to the self-operated-personnel only mode. The hybrid mode proved particularly suitable for scenarios with tight time windows, high vehicle travel costs, and limited job opportunities. The supply dynamics of crowdsourced personnel exhibited significant and stochastic impacts on delivery costs.

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更新日期/Last Update: 2025-10-21