基于软时间窗的 AGV配送路径规划研究
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摘要：
针对传统遗传算法应用于汽车总装生产线物料配送环节存在搜索效率低、易陷人局部最优、实用性差等缺点，提出了一种基于改进双种群遗传-模拟退火混合算法(improved dual population genetic-simulated annealinghybrid algorithm,IDPGSA)的自动导引车(automated guided vehicle,AGV)物料配送路径规划策略。首先该算法将初始种群划分为Ⅰ和Ⅱ两个种群分别进行寻优，以提高搜索效率；其次为了增加种群的多样性并避免过早收敛，对双种群分别引人顺序交叉与两点交叉两种交叉算子，irgibnnm 与滑动变异两种变异算子；最后在Ⅰ种群中引人逆转进化操作来提升全局寻优能力，而在Ⅱ种群中融合模拟退火算法增加局部搜索能力。经过仿真验证，改进后的策略在降低配送成本和提高配送效率方面表现更好,对于汽车总装生产线的优化有一定借鉴意义。
Aiming at the shortcomings of the traditional genetic algorithm applied to the material distribution link of the automobile assembly line, such as low search efficieney, easy to fall into local optimum, and poor practicability, an AGV(automated guided vehicle) material distribution path planning strategy was proposed based on improved dual population geneticsimulated annealing(lDPGSA) hybrid algorithm, First, the algorithm divides the initial population into two populations, Ⅰand Ⅱ, for optimization to improve the search efficiency: Second. in order to increase the diversity of the population and avoid premature convergence, two types of seguential crossover and two point crossover are introduced for the double population, along with irgibnm and sliding mutation two mutation operators；Finally, the reversevolution operation is introduced in the Ⅰ popuation to inprove the global optimization ability, and the simulated annealing algorithm is integrated in the Ⅱ population to in crease the local search ability, After simulation verification, the improved strategy performs better in reducing distribution costs and improving distribution eficiency, which has certain reference significance for the optimization of automobile assembly lines.
作者:
夏正龙，刘莹莹，韩德伟，杭津如，缪海鹏，韩秀虹
Xia Zhenglong，Liu Yingying，Han Dewei， Hang Jinru，Miao Haipeng， Han Xiuhong
机构地区：
江苏师范大学电气工程及自动化学院；连云港杰瑞自动化有限公司
引用本文：
夏正龙，刘莹莹，韩德伟等。基于软时间窗的 AGV 配送路径规划研究[J].学报(自然科学版)，2025,53(6):66-73.( Xia Zhenglong,Liu Yingying, Han Dewei, et al. Research on AGV distribution path planning based on soft time window[J].Journal of Henan Normal University( Natural Seienee Edition),2025,53(6)：66-73.DOI:10.16366/j.cnki.1000-2367.2024.07.08.0002.)
基金：
国家自然科学基金
关键词：
双种群；变异算子；混合算法；料配送；逆转进化
double population; mutation operator; hybrid algorithm; material distribution; reverse evolution
分类号：
TP242
