基于混合学习策略的可变速AGV与机器绿色集成调度  

Hybrid learning strategy for green integrated scheduling with variable speed AGV

作　　者：陈仁胜 吴斌[1] 闫飞一 CHEN Ren-sheng;WU Bin;YAN Fei-yi(School of Economics and Management,Nanjing Tech University,Nanjing 211816,China)

机构地区：[1]南京工业大学经济与管理学院,南京211816

出　　处：《控制与决策》2024年第12期3955-3963,共9页Control and Decision

基　　金：国家社科基金一般项目(20BGL025);国家重点研发项目子课题项目(2021YFB3301302);江苏省研究生科研与实践创新计划项目(KYCX23_1522)。

摘　　要：传统制造业正逐渐向智能化、绿色化制造模式转型.为实现柔性制造车间的增效减排,以最小化最大完工时间和总能耗为目标,构建充电约束下可变速AGV与机器绿色集成调度模型,并设计一种基于混合学习策略的改进NSGA-II算法进行优化求解.采用基于工序、机器、AGV及其速度的四段式染色体编码方案,对各编码段分别设计不同的交叉变异算子;采用基于反向学习的精英保留策略,以提高算法的种群多样性;提出针对问题特征的邻域搜索算子,基于Q-learning强化学习算法,动态调整迭代过程中的邻域结构,增强算法的局部搜索能力.最后通过仿真实验表明,改进NSGA-II算法是求解该问题的有效方法.The traditional manufacturing industry is gradually transitioning toward intelligent and environmentally friendly production modes.To achieve efficiency improvements and emissions reduction in flexible manufacturing workshops,this study aims to minimize makespan and total energy consumption.It constructs an integrated scheduling model for a variable-speed AGV and machine under charging constraint.An improved NSGA-II optimization algorithm is designed based on a hybrid learning strategy.This algorithm adopts a four-segment chromosome encoding scheme based on process,machines,AGV and AGV speed,with different crossover and mutation operators for each encoding segment.Additionally,an elite preservation strategy based on opposition-based learning is employed to enhance the algorithm's population diversity.Furthermore,a neighborhood search operator tailored to problem characteristics is proposed,utilizing the Q-learning reinforcement learning algorithm to dynamically adjust the neighborhood structure during the iteration process,thereby enhancing the algorithm's local search capabilities.Finally,the effectiveness of the improved NSGA-II in solving this problem is verified through simulation tests.

关 键 词：绿色集成调度 多目标优化 混合学习策略 可变速AGV NSGA-II 充电约束 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP23[自动化与计算机技术—控制科学与工程]