面向柔性作业车间动态调度的双系统强化学习方法

doi:10.16183/j.cnki.jsjtu.2021.215

摘要/Abstract

摘要：

航天结构件生产过程中批产任务与研发任务并存,个性化小批量研发生产任务导致紧急插单现象频发.为了保障任务如期完成,解决柔性作业车间面临的动态调度问题,以最小化设备平均负载和最小化总完工时间为优化目标,提出了感知-认知双系统驱动的双环深度Q网络方法.感知系统基于知识图谱实现对车间知识的表示并生成多维信息矩阵;认知系统将调度过程分别抽象为资源配置智能体和工序排序智能体两个阶段,分别对应两个优化目标,设计了车间状态矩阵对问题和约束进行描述,调度决策中分步骤引入动作指令;最后分别设计奖励函数实现资源配置决策和工序排序决策的评价.经某动力所航天壳体加工的实例验证和算法对比分析,验证了所提方法的优越性.

关键词: 感知-认知双系统, 双环深度Q网络, 动态调度, 知识图谱, 多智能体

Abstract:

In the production process of aerospace structural parts, there coexist batch production tasks and research and development (R&D) tasks. Personalized small-batch R&D and production tasks lead to frequent emergency insertion orders. In order to ensure that the task is completed on schedule and to solve the flexible job shop dynamic scheduling problem, this paper takes minimization of equipment average load and total completion time as optimization goals, and proposes a dual-loop deep Q network (DL-DQN) method driven by a perception-cognition dual system. Based on the knowledge graph, the perception system realizes the representation of workshop knowledge and the generation of multi-dimensional information matrix. The cognitive system abstracts the scheduling process into two stages: resource allocation agent and process sequencing agent, corresponding to two optimization goals respectively. The workshop status matrix is designed to describe the problems and constraints. In scheduling decision, action instructions are introduced step by step. Finally, the reward function is designed to realize the evaluation of resource allocation decision and process sequence decision. Application of the proposed method in the aerospace shell processing of an aerospace institute and comparative analysis of different algorithms verify the superiority of the proposed method.

Key words: perception-cognition dual system, dual-loop deep Q network (DL-DQN), dynamic scheduling, knowledge graph, multi-agent

中图分类号:

TP301

刘亚辉, 申兴旺, 顾星海, 彭涛, 鲍劲松, 张丹. 面向柔性作业车间动态调度的双系统强化学习方法[J]. 上海交通大学学报, 2022, 56(9): 1262-1275.

LIU Yahui, SHEN Xingwang, GU Xinghai, PENG Tao, BAO Jinsong, ZHANG Dan. A Dual-System Reinforcement Learning Method for Flexible Job Shop Dynamic Scheduling[J]. Journal of Shanghai Jiao Tong University, 2022, 56(9): 1262-1275.

图/表 20

表1

符号与变量

符号	符号描述
J	工件集合
G	设备组集合
M	设备集合
P	人员集合
S	物料集合
o_k_,_i_,_j	第k个任务中工件J_i的第j道工序
j	工序索引j=1, 2, …, m
R_k_,_i_,_j	第k个任务中工件J_i的第j道工序配置资源,R_k_,_i_,_j={M_k_,_i_,_j, P_k_,_i_,_j, S_k_,_i_,_j}
M_k_,_i_,_j	工序o_k_,_i_,_j的配置设备
P_k_,_i_,_j	工序o_k_,_i_,_j的配置操作人员
S_k_,_i_,_j	工序o_k_,_i_,_j的配置物料
$S k, i T$	第k个任务中工件J_i的到达时间
$s o k, i, j t$	工序o_k_,_i_,_j的开始时间
$e o k, i, j t$	工序o_k_,_i_,_j的结束时间
$r o k, i, j t$	工序o_k_,_i_,_j与下道工序的准备时间
$B k, i T$	第k个任务中工件J_i的总加工时间
W_l_,_t(W_k_,_i)	设备组G_l中设备M_t的加工负载(以设备为目标计算得到W_l_,_t,以工序为单位计算得到W_k_,_i)
$L l, t W$	设备组G_l中设备M_t的最大加工负载
$B M k, i, j J k, i$	0-1决策变量,取1时表示第k个任务中工件J_i在设备M_i_,_j上加工
D^P	交付期

表1

图1

图2

图3

表2

调度状态参数

调度状态	参数类型	表达式	含义
资源配置状态	人员状态 $f 1, 1 1$	$f 1,1, 1 1$ =P_k_,_i_,_j	人员类型
	物料状态 $f 1, 2 1$	$f 1, 2, 1 1$ =S_k_,_i_,_j	物料类型
	设备状态 $f 1,3 1$	$f 1, 3, 1 1$ =M_k_,_i_,_j	设备类型
		$f 1, 3, 2 1 = B M k, i, j J k, i = 0, 批产任务 k 中的工件 i 不在设备 M k, i, j 上加工 1, 批产任务 k 中的工件 i 在设备 M k, i, j 上加工$ (M_k_,_i_,_j∈M)	设备状态
工序排序状态	工艺状态 $f 1, 1 2$	$f 1, 1, 1 2$ =o_k_,_i_,_j	工艺状态
		$f 1, 1, 2 2$ =G_k_,_i_,_j	设备组状态
	时间状态 $f 1, 2 2$	$f 1, 2, 1 2$ = $s o k, i, j t$	开始时间
		$f 1, 2, 2 2$ = $e o k, i, j t$	结束时间
		$f 1, 2, 3 2$ = $r o k, i, j t$	运输时间
		$f 1, 2, 4 2$ = $D P k$	交付期

表2

表3

表4

工序排序决策动作

符号	描述	量化方式
FIFO	先到先加工优先规则	$a t 2 = m i n r k, i (r k, i 为释放时间)$
SPT	工序加工时间最短优先规则	$a t 2 = m i n ∑ k = 1 K ∑ i = 1 n (e o k, i, j t - s o k, i, j t + r o k, i, j t)$
EDD	交货期最早加工优先规则	$a t 2 = m i n D P k$
SL	松弛时间最短优先规则	$a t 2 = m i n D P k - x - ∑ j = 1 m B k, i T (x 为当前时间)$
SRPT	剩余加工时间最长优先规则	$a t 2 = m a x ∑ j = j' m B k, i T (j' 为当前工序)$

表4

表5

表6

图4

图5

表7

表8

表9

图6

表10

图7

图8

图9

表11

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决策方法	描述
决策1	若人员、设备当前工作环境中空闲且资源配料充足,选择某工件所需人员、设备、物料等资源,然后将其组合为资源配置方案.
决策2	若人员、设备当前工作环境中忙碌且资源配料不充足,分别判断人员已工作工时与技能参数、设备的负载量和资源的存储量,在满足设备负载量最优的情况下将其组合为资源配置方案.

算例	工件数目	工序数	设备数	工序加工时间/h
MK01	6	6	4	[1, 10]
MK02	6	6	6	[3, 8]
MK03	6	8	6	[2, 6]
MK04	8	6	4	[1, 10]
MK05	8	8	6	[2, 9]
MK06	10	6	4	[1, 10]
MK07	10	6	6	[3, 8]
MK08	10	8	6	[2, 6]
MK09	12	6	4	[1, 10]
MK10	12	8	6	[2, 9]

工序	设备组	设备组序列
固溶	固溶炉组	G₁
旋压	旋压机组	G₂
s退火	退火炉组	G₃
时效	时效炉组	G₄
粗加工/精加工	数控机床组/加工中心组	G₅
电子束组焊/激光焊	焊机组	G₆
油淬	油淬炉组	G₇
氮气淬	氮气淬炉组	G₈
回火	回火炉组	G₉

任务	工序1	工序2	工序3	工序4	工序5	工序6	工序7	工序8
J₁	固溶	旋压	时效	粗加工	焊接	氮气淬	回火	精加工
J₂	旋压	退火	粗加工	焊接	氮气淬	回火	精加工
J₃	固溶	旋压	粗加工	焊接	油淬	回火	精加工
J₄	固溶	旋压	退火	粗加工	焊接	油淬	回火	精加工
J₅	固溶	旋压	退火	粗加工	焊接	精加工
J₆	固溶	旋压	时效	粗加工	焊接	氮气淬	回火	精加工

设备类别	设备型号
普通卧式车床	CDZ6140-1, CDZ6140-2
	CD6140B-1
数控卧式车床	CK61200W-1
	CK6146A-1
	CK64160-1, CK64160-2, CK64160-3
	CK64250-1
CKD系列数控车床	CKD6163-1
	CKD6163K-1, CKD6163K-2
	CKD6140S-1
	CKD6180D-1, CKD6180D-2
管螺纹车床	QK1319A-1, QK1319A-2