摘要
针对带时间窗的时间依赖型同时取送货车辆路径问题(Time Dependent Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows,TDVRPSPDTW),本文建立以车辆固定成本、驾驶员成本、燃油消耗及碳排放成本之和为优化目标的数学模型;并在传统蚁群算法的基础上,利用节约启发式构造初始解初始化信息素,改进状态转移规则,引入局部搜索策略,提出一种带自适应大邻域搜索的混合蚁群算法(Ant Colony Optimization with Adaptive Large Neighborhood Search,ACO-ALNS)进行求解;最后,分别选取基准问题算例和改编生成TDVRPSPDTW算例进行实验。实验结果表明:本文提出的ACO-ALNS算法可有效解决TDVRPSPDTW的基准问题;相较于模拟退火算法和带局部搜索的蚁群算法,本文算法求解得到的总配送成本最优值平均分别改善7.56%和2.90%;另外,相比于仅考虑碳排放或配送时间的模型,本文所构建的模型综合多种因素,总配送成本平均分别降低4.38%和3.18%,可有效提高物流企业的经济效益。
To solve the time-dependent vehicle routing problem with simultaneous pickup-delivery and time windows(TDVRPSPDTW),this paper proposes a mathematical model with the sum of vehicle fixed cost,driver cost,fuel consumption and carbon emission cost as the optimization objective.Based on the traditional ant colony optimization,this paper introduces a hybrid ant colony optimization with adaptive large neighborhood search(ACO-ALNS).It uses heuristic initialization of pheromones,improves state transition rules,and uses local search strategies to improve solution quality.Benchmark problem instances and adapted TDVRPSPDTW instances are utilized for experimentation.The experimental results demonstrate the effectiveness of the proposed ACO-ALNS algorithm in solving the benchmark problem of TDVRPSPDTW.Compared to the simulated annealing and ant colony optimization with local search,the proposed algorithm improves the optimal value of total distribution cost by an average of 7.56%and 2.90%,respectively.In addition,the presented model incorporates multiple factors,resulting in an average reduction of 4.38%and 3.18%in total distribution costs compared to models that only consider carbon emissions or delivery time.This improvement can effectively enhance the economic benefits of logistics enterprises.
作者
何美玲
杨梅
韩珣
武晓晖
HE Meiling;YANG Mei;HAN Xun;WU Xiaohui(School of Automotive and Traffic Engineering,Jiangsu University,Zhenjiang 212013,Jiangsu,China;Intelligent Policing Key Laboratory of Sichuan Province,Sichuan Police College,Luzhou 646000,Sichuan,China;Department of Transportation Management,Sichuan Police College,Luzhou 646000,Sichuan,China)
出处
《交通运输系统工程与信息》
EI
CSCD
北大核心
2024年第4期231-242,262,共13页
Journal of Transportation Systems Engineering and Information Technology
基金
教育部人文社会科学研究青年基金(21YJCZH180)
智能警务四川省重点实验室开放课题(ZNJW2023KFMS004)
江苏省研究生科研创新计划项目(KYCX22_3674)。
关键词
物流工程
同时取送货车辆路径问题
蚁群算法
时间依赖
时间窗
logistics engineering
vehicle routing problem with simultaneous pickup-delivery
ant colony optimization
time-dependent
time windows
