基于强化学习的群体进化算法求解双边多目标同步并行拆解线平衡问题

Reinforcement Learning-based Swarm Evolutionary Algorithm To Solve Two-sided Multi-objective Synchronous Parallel Disassembly Line Balancing Problem

双碳背景下,工业制造领域朝着绿色、节能的方向转型,对废旧产品进行回收拆解以及再制造有利于推动高质量发展。论文针对大型复杂产品拆解过程中难以改变拆卸方向的问题,并综合考虑实际拆卸过程中任务之间存在的多种约束类型,研究了同步并行模式下的双边拆解线平衡问题(two-sided disassembly line balancing problem, TDLBP)。首先引入双边布局的拆解线模式,定义了与优先和或优先关系,建立了TDLBP的数学模型以优化产线布置、经济效益和安全环保三个方面共六个指标。然后提出了一种基于强化学习的群体进化算法,采用Q-learing利用所学知识选择迭代中的最佳算子,通过拥挤距离筛选Pareto解集,利用精英保留策略加速算法收敛,进而高效获取近似最优的拆解方案。最后通过求解小规模算例并对比分析,验证了所提出算法的有效性和优越性,并进行大规模案例的应用。

Under the background of dual carbon,the industrial manufacturing field is transforming in the direction of green and energy-saving.The recycling,disassembling and remanufacturing of waste products is conducive to promoting high-quality development.Aiming at the problem that it is difficult to change the disassembly direction during the disassembly process of large and complex products,and comprehensively considering the various constraints that exist between tasks in the actual disassembly process,the paper studies the two-sided disassembly line balancing problem(TDLBP)in the synchronous parallel mode.Firstly,the disassembly line mode of two-sided layout is introduced,and the relationship of and priority and or priority is defined,and the mathematical model of TDLBP is established to optimize the layout of the production line,economic benefits,safety and environmental protection with a total of six indicators.Then,a swarm evolutionary algorithm based on reinforcement learning is proposed.Q-learning is used to select the best operator in the iteration by using the knowledge learned,the Pareto solution set is screened by the crowding distance,and the elite retention strategy is used to accelerate the convergence of the algorithm,so as to efficiently obtain the approximate optimal disassembly scheme.Finally,the effectiveness and superiority of the proposed algorithm are verified by solving small-scale cases and comparative analysis,and the application of large-scale case is carried out.