摘要
针对我国物流共同配送运营成本居高不下以及碳排放较高的问题,构建以固定成本、运输成本、碳排放成本及时间惩罚成本等物流运营总成本最小的共同配送路径优化模型,并设计改进遗传算法来求解该模型。结合不同规模的算例,基于MatlabR2024a平台进行仿真实验。结果表明:相较于传统遗传算法,改进遗传算法在求解共同配送路径优化问题上表现出显著的优势;在碳排放约束机制下,改进遗传算法求解的配送方案可以有效提高车辆调度率,显著降低共同配送的总成本。
Aiming at the problems of high operation cost and high carbon emission of logistics common distribution in China,we construct a common distribution path optimization model that minimizes the total cost of fixed cost,transportation cost,carbon emission cost,and time penalty cost and design an improved genetic algorithm to solve the model.The model is constructed to minimize the total cost of logistics operation including fixed cost,transportation cost and time penalty cost,and the improved genetic algorithm is designed to solve the model.Simulation experiments are carried out based on MatlabR2024a platform with different sizes of examples.The results show that compared with the traditional genetic algorithm,the improved genetic algorithm shows significant advantages in solving the codistribution path optimization problem;under the carbon emission constraint mechanism,the distribution scheme solved by the improved genetic algorithm can effectively improve the vehicle scheduling rate and significantly reduce the total cost of co-distribution.
作者
宾厚
万茹梦
刘妃
Bin Hou;Wan Rumeng;Liu Fei(School of Economics and Management,Hunan University of Technology,Zhuzhou,Hunan 412007)
出处
《绥化学院学报》
2025年第8期148-152,共5页
Journal of Suihua University
基金
湖南省社会科学成果评审委员会立项课题(XSP25YBZ181)
湖南省研究生科研创新项目(LXBZZ2024325)。
关键词
共同配送
碳排放
改进遗传算法
路径优化
common distribution
carbon emission
improved genetic algorithm
path optimization
