基于改进免疫遗传算法的铁路空箱调运优化  

Optimization of Railway Empty Container Repositioning Based on Improved Immune Genetic Algorithm

作　　者：缪莉盼 张宇峰 水源 向万里[1] 王文利[3] MIAO Lipan;ZHANG Yufeng;SHUI Yuan;XIANG Wanli;WANG Wenli(School of Traffic and Transportation,Lanzhou Jiaotong University,Lanzhou 730070,China;Lanzhou Railway Logistics Center,China Railway Lanzhou Group Co.,Ltd.,Lanzhou 730030,China;School of Economics and Management,Lanzhou Jiaotong University,Lanzhou 730070,China)

机构地区：[1]兰州交通大学交通运输学院,甘肃兰州730070 [2]中国铁路兰州局集团有限公司兰州铁路物流中心,甘肃兰州730030 [3]兰州交通大学经济管理学院,甘肃兰州730070

出　　处：《物流科技》2025年第3期121-125,共5页Logistics Sci Tech

基　　金：国家自然科学基金项目(62262037);甘肃省科技厅软科学项目(23JRZA360);中国铁路兰州局集团有限公司科技项目(LZJKY2024012-1)。

摘　　要：针对空箱调运问题,在传统免疫遗传算法(Immune Genetic Algorithm,IGA)基础上,通过对Hamming距离计算方式优化与调整,获得抗体亲和度与抗体浓度的精确度量,并据此设计了改进免疫遗传算法(Improved Immune Genetic Algorithm,IIGA)。为验证该算法的有效性和优势,以兰州铁路局下辖的各集装箱办理站实际运输数据为例,分别将所提出的IIGA与传统IGA、遗传算法(Genetic Algorithm,GA)设计对比实验。结果表明:在解决空箱调运问题时,相较于传统IGA与GA,IIGA能够在相对较短的时间内,以最少的迭代次数迅速收敛至最优解。Addressing the issue of empty container repositioning,an Improved Immune Genetic Algorithm(IIGA)was developed based on the traditional Immune Genetic Algorithm(IGA)by optimizing and adjusting the calculation method for Hamming distance to achieve precise measurements of antibody affinity and antibody concentration.To validate the effectiveness and advantages of this algorithm,a comparative experiment was designed using actual transportation data from various container terminals under the jurisdiction of Lanzhou Railway Bureau,comparing the proposed IIGA with traditional IGA and Genetic Algorithm(GA).The results indicated that,when solving the problem of empty container repositioning,IIGA was able to converge to the optimal solution more rapidly with the least number of iterations within a relatively shorter period of time compared to traditional IGA and GA.

关 键 词：改进免疫遗传算法 空箱调运 集装箱 HAMMING距离 亲和度 

分 类 号：F253[经济管理—国民经济]