基于改进遗传算法的低碳冷链物流配送路径优化  

Low-carbon Cold Chain Logistics Distribution Path Optimization Based on Improved Genetic Algorithm

作　　者：王金玉 袁凯 宋冰 胡冰[1,2] 孙知信[1,2] WANG Jin-yu;YUAN Kai;SONG Bing;HU Bing;SUN Zhi-xin(Post Big Data Technology and Application Engineering Research Center of Jiangsu Province,Nanjing University of Posts and Telecommunications,Nanjing 210003;Post Industry Technology Research and Development Center of the State Posts Bureau(Internet of Things Technology),Nanjing University of Posts and Telecommunications,Nanjing 210003;AnhuiYougu Express Intelligent Technology Co.,Ltd.,Wuhu 241399,China)

机构地区：[1]南京邮电大学江苏省邮政大数据技术与应用工程研究中心,江苏南京210003 [2]南京邮电大学国家邮政局邮政行业技术研发中心(物联网技术),江苏南京210003 [3]安徽邮谷快递智能科技有限公司,安徽芜湖241399

出　　处：《物流工程与管理》2024年第11期26-31,共6页Logistics Engineering and Management

摘　　要：学界对于带时间窗的路径优化问题早有研究,但传统算法受限于无法收敛或局部最优等问题。为此,文中提出一种改进遗传算法,以低碳冷链物流配送为应用场景,构建综合总成本最小化模型,包括车辆固定成本、运输成本、碳排放成本及超时惩罚成本等。算法通过佳点集改进初始解分布,避免局部最优,并优化交叉和变异过程以提高效率。此外,引入邻域搜索策略以扩大搜索范围。实验采用PyCharm软件,结果显示改进遗传算法在求解速度和搜索能力上优于传统及自适应遗传算法。The path optimization problem with time windows has long been studied in academia,but the traditional algorithms are limited by problems such as inability to converge or local optimization.For this reason,this paper proposes an improved genetic algorithm to construct a comprehensive total cost minimization model with low-carbon cold chain logistics and distribution as the application scenario,including vehicle fixed cost,transportation cost,carbon emission cost and overtime penalty cost.The algorithm improves the initial solution distribution through the good point set to avoid local optimum,and optimizes the crossover and mutation processes to improve efficiency.In addition,a neighborhood search strategy is introduced to expand the search range.The experiments are conducted by using PyCharm software,and the results show that the improved genetic algorithm outperforms the traditional and adaptive genetic algorithms in terms of solution speed and search capability.

关 键 词：路径优化 佳点集 邻域搜索 冷链 遗传算法 

分 类 号：F252[经济管理—国民经济] TP18[自动化与计算机技术—控制理论与控制工程]