Anylogic-based Platform-Enterprise Collaborative Scheduling Simulation System for Cloud Manufacturing

doi:10.16182/j.issn1004731x.joss.24-0393

Abstract

Abstract:

Aiming at the lack of research on collaborative scheduling between a cloud manufacturing platform and associated enterprises, as well as the lack of simulation systems to simulate scheduling strategy combinations and to visualize dynamic scheduling processes, a simulation system that supports visualization of cloud manufacturing platform-enterprise collaborative dynamic scheduling processes is designed and developed. System requirements are analyzed in detail, and then a scalable platform-enterprise collaborative scheduling model and system functional architecture based on hierarchical multi-agents is proposed. Combined with a case of supply chain of industrial robots, considering random selection, time optimal strategy in the cloud manufacturing platform layer and the combination of FIFO (first in first out), SPT (shortest processing time), EDD (earliest due date) in the enterprise layer, a simulation model of platform-enterprise collaborative scheduling is constructed. The system is implemented based on Anylogic, and simulation experiments are conducted through the combination of multiple strategies and use cases. Simulation experiments indicate that the system has functions of manufacturing scenario scalability and dynamic visualization, and is able to simulate, test and optimize performance of scheduling policy combinations at the platform layer and the enterprise layer, respectively, so as to provide a simulation environment and verification platform for cloud manufacturing platform-enterprise collaborative scheduling strategies.

Key words: cloud manufacturing, platform-enterprise collaboration, scheduling, simulation system, Anylogic

CLC Number:

TP391.9

Wang Linxuan, Liu Yongkui, Zhang Lin, Lin Tingyu, Wang Lihui. Anylogic-based Platform-Enterprise Collaborative Scheduling Simulation System for Cloud Manufacturing[J]. Journal of System Simulation, 2025, 37(9): 2225-2241.

Figures/Tables 24

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Table 1

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Fig. 5

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Table 5

Initialization information on manufacturing resources

Sheet	说明	数据
Sheet1	伺服电机制造商参数	$D j, k$ 、 $L j, k$
Sheet2	控制器制造商参数
Sheet3	减速器制造商参数
Sheet4	智能芯片制造商参数
Sheet5	机器人本体制造商参数
Sheet6	产线、设备与资源参数	$p x$ 、 $a t i m e$ 、 $l t i m e$

Table 5

Table 6

Fig. 9

Fig. 10

Fig. 11

Table 7

Attributes of order Agents

订单	需求量	交货期/s	订单	需求量	交货期/s
$T 1$	6	519.61	$T 9$	1	150.96
$T 2$	8	1 045.33	$T 10$	10	1 110.41
$T 3$	4	538.70	$T 11$	4	392.32
$T 4$	5	670.07	$T 12$	6	733.50
$T 5$	7	927.87	$T 13$	13	1 500.96
$T 6$	3	434.76	$T 14$	11	775.13
$T 7$	2	271.84	$T 15$	12	1 498.04
$T 8$	9	1 144.27

Table 7

Table 8

Attributes of enterprise Agent, logistics Agent and equipment Agent

企业名称	服务类型	地理位置	初始节拍时间	货车数量	物流速度	流水线速度	升降机速度	零部件/机械臂组装速度	控制系统组装速度	传感器执行器组装速度	调试检验速度
$e 1$	伺服电机	(34.5°N，109.1°E)	20	4	230
$e 2$		(34.8°N，108.5°E)		4	230
$e 3$		(35.0°N，107.6°E)		4	230
$e 4$		33.9°N，108.5°E)		4	230
$e 5$	控制器	(34.3°N，108.9°E)	20	4	230
$e 6$		(34.8°N，107.9°E)		4	230
$e 7$		(35.0°N，109.8°E)		4	230
$e 8$		(33.8°N，108.1°E)		4	230
$e 9$	减速器	(34.8°N，107.3°E)	30	4	230
$e 10$		(34.2°N，107.9°E)		4	230
$e 12$		(33.8°N，109.0°E)		4	230
$e 12$		(35.0°N，108.9°E)		4	230
$e 13$	智能芯片	(34.9°N，109.1°E)	25	4	230
$e 14$		(33.9°N，107.5°E)		4	230
$e 15$		(35.0°N，108.5°E)		4	230
$e 16$		(34.1°N，109.5°E)		4	230
$e 17$	机器人本体	(34.5°N，107.5°E)	126.9			0.225	1	$8 ± 1$	$10 ± 1$	$6 ± 1$	$4 ± 1$
$e 18$		(34.4°N，108.6°E)	129.2			0.225	1
$e 19$		(34.5°N，109.5°E)	126.4			0.225	1
$e 20$		(33.9°N，108.9°E)	126.2			0.225	1

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References 24

[1]	Wang Xiaohan, Zhang Lin, Liu Yongkui, et al. Logistics-involved Task Scheduling in Cloud Manufacturing with Offline Deep Reinforcement Learning[J]. Journal of Industrial Information Integration, 2023, 34: 100471.
[2]	Ping Yaoyao, Liu Yongkui, Zhang Lin, et al. Sequence Generation for Multi-task Scheduling in Cloud Manufacturing with Deep Reinforcement Learning[J]. Journal of Manufacturing Systems, 2023, 67: 315-337.
[3]	Liu Saibo, Deng Qianwang, Liu Xiahui, et al. Dual-service Integrated Scheduling of Manufacturing and Logistics for Multiple Tasks in Cloud Manufacturing[J]. Expert Systems with Applications, 2024, 235: 121129.
[4]	Liu Yongkui, Ping Yaoyao, Zhang Lin, et al. Scheduling of Decentralized Robot Services in Cloud Manufacturing with Deep Reinforcement Learning[J]. Robotics and Computer-Integrated Manufacturing, 2023, 80: 102454.
[5]	Yuan Minghai, Cai Xianxian, Zhou Zhuo, et al. Dynamic Service Resources Scheduling Method in Cloud Manufacturing Environment[J]. International Journal of Production Research, 2021, 59(2): 542-559.
[6]	Zhang Han, Li Kai, Chu Chengbin, et al. Parallel Batch Processing Machines Scheduling in Cloud Manufacturing for Minimizing Total Service Completion Time[J]. Computers & Operations Research, 2022, 146: 105899.
[7]	赵秋云, 魏乐, 舒红平. 面向制造任务的云制造虚拟车间构造方法[J]. 计算机应用, 2021, 41(7): 2003-2011.
	Zhao Qiuyun, Wei Le, Shu Hongping. Construction Method of Cloud Manufacturing Virtual Workshop for Manufacturing Tasks[J]. Journal of Computer Applications, 2021, 41(7): 2003-2011.
[8]	Wan Changcheng, Zheng Hualin, Guo Liang, et al. Hierarchical Scheduling for Multi-composite Tasks in Cloud Manufacturing[J]. International Journal of Production Research, 2023, 61(4): 1039-1057.
[9]	Liu Yongkui, Wang Lihui, Wang Yuquan, et al. Multi-agent-based Scheduling in Cloud Manufacturing with Dynamic Task Arrivals[J]. Procedia CIRP, 2018, 72: 953-960.
[10]	Chen Zhen, Zhang Lin, Wang Xiaohan, et al. Cloud-edge Collaboration Task Scheduling in Cloud Manufacturing: An Attention-based Deep Reinforcement Learning Approach[J]. Computers & Industrial Engineering, 2023, 177: 109053.
[11]	Jian Chengfeng, Ping Jing, Zhang Meiyu. A Cloud Edge-based Two-level Hybrid Scheduling Learning Model in Cloud Manufacturing[J]. International Journal of Production Research, 2021, 59(16): 4836-4850.
[12]	Yang Chen, Liao Fangyin, Lan Shulin, et al. Flexible Resource Scheduling for Software-defined Cloud Manufacturing with Edge Computing[J]. Engineering, 2023, 22: 60-70.
[13]	Yuanjun Laili, Wang Xiaohan, Zhang Lin, et al. DSAC-configured Differential Evolution for Cloud-edge-device Collaborative Task Scheduling[J]. IEEE Transactions on Industrial Informatics, 2024, 20(2): 1753-1763.
[14]	北京航空航天大学. 基于多智体的云制造仿真系统和方法: CN201610997856.X[P]. 2017-02-01.
[15]	北京航空航天大学. 一种X语言混合建模平台及建模方法: CN202111027918.1[P]. 2021-11-19.
[16]	北京航空航天大学. 一种X语言混合模型建模系统、方法及存储介质: CN202111027923.2[P]. 2021-11-19.
[17]	付超, 王冬越, 李鑫鑫, 等. 自动化生产线柔性制造过程优化决策仿真平台[J]. 系统仿真学报, 2023, 35(5): 920-935.
	Fu Chao, Wang Dongyue, Li Xinxin, et al. Simulation Platform for Optimization and Decision Making of Flexible Manufacturing Process of Automatic Production Lines[J]. Journal of System Simulation, 2023, 35(5): 920-935.
[18]	刘志峰, 陈伟, 杨聪彬, 等. 基于数字孪生的零件智能制造车间调度云平台[J]. 计算机集成制造系统, 2019, 25(6): 1444-1453.
	Liu Zhifeng, Chen Wei, Yang Congbin, et al. Intelligent Manufacturing Workshop Dispatching Cloud Platform Based on Digital Twins[J]. Computer Integrated Manufacturing Systems, 2019, 25(6): 1444-1453.
[19]	Chen T, Wang Yucheng. Estimating Simulation Workload in Cloud Manufacturing Using a Classifying Artificial Neural Network Ensemble Approach[J]. Robotics and Computer-Integrated Manufacturing, 2016, 38: 42-51.
[20]	赵淳, 张霖, 陶飞, 等. 基于云制造平台企业合作演化仿真系统的设计与实现[C]//第九届中国多智能体系统与控制会议(MASC2013)论文集. 焦作: 中国系统仿真学会, 2014: 1-5.
[21]	张莉, 杨子天, 葛宁, 等. 面向生产计划分析的协同制造过程建模语言及建模仿真系统[J]. 计算机集成制造系统, 2023, 29(5): 1415-1428.
	Zhang Li, Yang Zitian, Ge Ning, et al. Production Plan Analysis Oriented Collaborative Manufacturing Process Modeling Language and Modeling/Simulation System[J]. Computer Integrated Manufacturing Systems, 2023, 29(5): 1415-1428.
[22]	刘永奎, 曾鸣, 张霖, 等. 基于微服务架构的云制造调度仿真系统设计与开发[J]. 系统仿真学报, 2022, 34(4): 700-711.
	Liu Yongkui, Zeng Ming, Zhang Lin, et al. Design and Development of a Simulation System for Scheduling in Cloud Manufacturing Based on Microservice Architecture[J]. Journal of System Simulation, 2022, 34(4): 700-711.
[23]	潘俊浩. 面向工业机器人的云制造仿真服务系统的研究与开发[D]. 厦门: 厦门大学, 2017.
	Pan Junhao. Research and Development of Simulation Service System with Cloud Manufacturing for Industrial Robot[D]. Xiamen: Xiamen University, 2017.
[24]	Chen T, Chiu M C. Development of a Cloud-based Factory Simulation System for Enabling Ubiquitous Factory Simulation[J]. Robotics and Computer-Integrated Manufacturing, 2017, 45: 133-143.

名称	类型	数量	说明
TaskFlow	智能体群	1	订单
MotorResource	智能体群	1	伺服电机资源集
ControllerResource	智能体群	1	控制器资源集
RudecerResource	智能体群	1	减速器资源集
ChipResource	智能体群	1	芯片资源集
RobotResource	智能体群	1	本体制造资源集

Agent	名称	类型	数量	说明
RobotResource的企业Agent	RobotEnt1	单智能体	1	本体制造商1
	RobotEnt2	单智能体	1	本体制造商2
	RobotEnt3	单智能体	1	本体制造商3
	RobotEnt4	单智能体	1	本体制造商4
其余4种资源集企业Agent	MSupplier	智能体群	4	伺服电机供应商
	CSupplier	智能体群	4	控制器供应商
	RSupplier	智能体群	4	减速器供应商
	ChSupplier	智能体群	4	芯片供应商

Agent	名称	类型	数量	说明
设备Agent	ProduceOnCrane	智能体群	3	托盘
	MaterialBatch	智能体群	1	封装机
	AGV	单智能	1	AGV
物流Agent	Truck	智能体群	4	货车
物流Agent	Materials	智能体群	1	加工原料

名称	含义	取值
combobox1	平台调度策略选择	随机/时间最优调度
combobox2-5	企业调度策略选择	FIFO/SPT/EDD
editbox	订单需求数量设置	(1,20)
radio	需求数量生成方式	随机生成/用户设定
button1	提交并产生订单
button2	添加新的调度策略	关联scheduling.txt

平台调度方式	企业调度方式	平台订单制造总周期/h	企业加工总时间/s	企业延误总时间/s	企业加工数量	企业平均完工时间/s	企业平均延误交货期/s	企业节拍时间/s	调度结果
随机调度	FIFO	40.36	1 833.60	5 697.81	31	59.15	183.80	126.25	1—2—3—13
	SPT		2 187.26	1 033.79	16	136.70	129.96	129.95	8—9—12
	EDD		3 191.46	6 193.12	31	102.95	126.01	126.01	4—7—6—10—14
	FIFO		1 102.54	2 524.23	23	47.93	125.92	125.92	5—11—15
时间最优调度	FIFO	22.65	1 561.28	5 191.34	33	47.31	157.31	125.95	2—6—10—15
	SPT		1 662.02	2 996.77	25	66.48	119.87	129.83	3—9—11—4—14
	EDD		1 162.80	3 918.10	24	48.45	163.25	125.97	7—8—13
	FIFO		1 249.56	2 524.23	19	65.77	132.85	126.46	1—5—12