改进遗传算法的带时间窗车辆配送路径优化  

Optimization of Vehicle Delivery Path with Time Window Based on Improved Genetic Algorithm

作　　者：李欢 Li Huan(College of Traffic and Transportation,Xi'an Traffic Engineering Institute,Xi'an 710300,China)

机构地区：[1]西安交通工程学院交通运输学院,西安710300

出　　处：《科技通报》2025年第4期36-41,共6页Bulletin of Science and Technology

基　　金：陕西省教育厅科学研究计划项目资助(24JK0537)。

摘　　要：为提高顾客的满意度,配送服务需严格按顾客规定的时间要求执行。合理地对车辆配送路径进行优化,以适应有时间限制的配送任务,是解决这一挑战的关键策略。通过构建以时间窗为约束条件的车辆配送路径模型,并利用改进的遗传算法对该模型进行求解,在传统遗传算法框架之上,重新构建适应度函数,对交叉与变异机制设计自适应调整方法,并优化算法的终止条件,以提高求解效率和准确性。为验证所提出改进方法的有效性,实验使用标准数据集进行测试,并将改进的遗传算法与传统遗传算法、蚁群算法以及文献中的其他优化方法进行比较分析。实验结果表明,提出的改进算法能够有效避免陷入局部最优,在客户规模较大情况下求解精确性高,并在迭代初期能迅速收敛,与最优解的最大偏差仅为3.83%,证明其在求解效率和解的质量上的优势。To improve customer satisfaction,delivery services must strictly follow the customer's specified time requirements.Therefore,optimizing the vehicle delivery route to adapt to time-limited delivery tasks is a key strategy to solve this challenge.By establishing a vehicle delivery route model with time window constraints and optimizing it using an optimized genetic algorithm,the proposed method can effectively solve the problem.In the traditional genetic algorithm framework,the fitness function is reconstructed,and the adaptive adjustment methods for crossover and mutation are designed.The termination conditions of the algorithm are optimized to improve the solution efficiency and accuracy.To verify the effectiveness of the proposed improved method,the standard case in CVRPLIB(capacitated vehicle routing problem library) is used for testing,and the improved genetic algorithm is compared with GA(genetic algorithm),ACO(ant colony optimization),and other optimization methods in the literature.The experimental results show that the proposed improved algorithm can effectively avoid falling into the local optimal,has high solving accuracy in the case of large customer scale,and can rapidly converge in the initial iteration,and the maximum deviation from the optimal solution is only 3.83%,which proves its advantages in solving efficiency and quality.

关 键 词：遗传算法 改进方法 时间窗 车辆配送 路径优化 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]