基于DDQN算法的混流车间作业动态自适应调度的研究  

Research on Operation Dynamic Adaptive Scheduling of Hybrid Flow Workshop Based on DDQN Algorithm

作　　者：陈晓航 王美林[1] 吴耿枫 梁凯晴 CHEN Xiaohang;WANG Meiling;WU Gengfeng;LIANG Kaiqing(Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学,广东广州510006

出　　处：《现代信息科技》2021年第24期133-137,141,共6页Modern Information Technology

基　　金：国家自然科学基金(U1701266);广东省科技计划(2019A050513011、2017B090901056);广州市科技计划(202002030386)。

摘　　要：大规模生产的混流车间制造系统存在资源规模大、约束多等问题,快速找到合适的调度策略是实现高效生产的关键。为解决传统数学规划算法和启发式算法存在的策略求解效率低、自适应性差等问题,文章提出一种基于DDQN的智能车间动态自适应调度方法,对车间作业的自适应调度做了研究。通过“一步一推理”的自适用动态调度,可以高效地匹配合适的调度策略动作。In view of the large scale of resources and many constraints of the hybrid flow workshop manufacturing system in mass production,how to quickly find a suitable scheduling strategy is the key to achieve efficient production.In order to solve the problems of low strategy solving efficiency and poor adaptive existing in traditional mathematical programming algorithms and heuristic algorithms,this paper proposes a dynamic adaptive scheduling method for intelligent workshop based on DDQN,research on adaptive scheduling of workshop operations.Through the self-adaptive dynamic scheduling of“one step,one reasoning”,the appropriate scheduling policy actions can be efficiently matched.

关 键 词：深度强化学习 DDQN算法 动态自适应调度 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]