摘要
在城市垃圾量急剧增加的背景下,为解决清运车辆过度使用导致碳排放持续增加问题,提出一种垃圾清运车辆低碳路径优化方法。在考虑车辆容量限制和时间窗约束的情况下,综合多个清运过程影响因素优化目标函数,建立以路径最优、成本极小化为目标的数学模型。设计了改进模拟退火算法求解模型,改进策略包括使用K-means聚类算法协助模拟退火算法,加入2-shift法、2-symmetry法和2-insert法,使算法在全局搜索和局部搜索达到平衡,在邻域搜索上引入随机均匀采样策略,避免因穷举带来的时间复杂度过高问题。多个经典算例的仿真实验结果表明,改进模拟退火算法对优化车辆低碳路径规划模型具有较好的收敛速度和鲁棒性。
In order to solve the problem of the continuous increase of carbon emission due to the overuse of waste removal vehicles in the context of the rapid increase of urban garbage volume,a low-carbon path optimization method for waste removal vehicles was proposed.Under the consideration of vehicle capacity limitation and time window constraints,the objective function was optimized by integrating several influencing factors of the removal process,and a mathematical model with the objectives of path optimization and cost minimization was established.The improved simulated annealing algorithm was designed to solve the model,and the improvement strategies include using K-means clustering algorithm to assist the simulated annealing algorithm,adding 2-shift method,2-symmetry method and 2-insert method to make the algorithm reach a balance between global and local search,and introducing a random uniform sampling strategy in the neighborhood search to avoid the problem of time complexity brought by exhaustive search.Simulation experiments were carried out through several classical examples,and the results showed that the improved simulated annealing algorithm had better performance in optimizing the low-carbon path planning model for vehicles,with better convergence speed and robustness.
作者
邓嘉鑫
唐宏伟
何厚为
刘书剑
周纯清
李佳乐
DENG Jiaxin;TANG Hongwei;HE Houwei;LIU Shujian;ZHOU Chunqing;LI Jiale(Hunan Provincial Engineering Technology Research Center of Electric Energy Conversion and Control for Special Equipment,Shaoyang University,Shaoyang 422000,Hunan,China;Hunan Provincial Key Laboratory of Operation and Control of Multi-Power Supply Grid,Shaoyang University,Shaoyang 422000,Hunan,China)
出处
《农业装备与车辆工程》
2024年第9期46-52,共7页
Agricultural Equipment & Vehicle Engineering
基金
国家级大学生创新创业训练计划项目(202210547018)
湖南省自然科学基金(2022JJ50205)。
关键词
车辆低碳路径优化
改进模拟退火算法
K-MEANS聚类算法
随机均匀采样策略
vehicle low-carbon path optimization
improved simulated annealing algorithm
K-means clustering algorithm
random uniform sampling strategy
