Scheduling optimization of task allocation in integrated manufacturing system based on task decomposition

被引:0
作者
Aijun Liu [1 ,2 ]
Michele Pfund [2 ]
John Fowler [2 ]
机构
[1] Department of Management Engineering, School of Economics & Management, Xidian University
[2] Department of Supply Chain Management, WP Carey School of Business, Arizona State University
关键词
integrated manufacturing system; optimization; task decomposition; task scheduling;
D O I
暂无
中图分类号
TB497 [技术管理];
学科分类号
08 ;
摘要
How to deal with the collaboration between task decomposition and task scheduling is the key problem of the integrated manufacturing system for complex products. With the development of manufacturing technology, we can probe a new way to solve this problem. Firstly, a new method for task granularity quantitative analysis is put forward, which can precisely evaluate the task granularity of complex product cooperation workflow in the integrated manufacturing system, on the above basis; this method is used to guide the coarse-grained task decomposition and recombine the subtasks with low cohesion coefficient. Then, a multi-objective optimieation model and an algorithm are set up for the scheduling optimization of task scheduling. Finally, the application feasibility of the model and algorithm is ultimately validated through an application case study.
引用
收藏
页码:422 / 433
页数:12
相关论文
共 50 条
[21]   Cloud Task Scheduling Based on Ant Colony Optimization [J].
Tawfeek, Medhat A. ;
El-Sisi, Ashraf ;
Keshk, Arabi E. ;
Torkey, Fawzy A. .
2013 8TH INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING & SYSTEMS (ICCES), 2013, :64-69
[22]   Cloud Task Scheduling Based on Ant Colony Optimization [J].
Tawfeek, Medhat ;
El-Sisi, Ashraf ;
Keshk, Arabi ;
Torkey, Fawzy .
INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2015, 12 (02) :129-137
[23]   A task scheduling algorithm based on chemical reaction optimization [J].
Ouyang, Liduo ;
Xu, Cheng ;
Zeng, Lining .
Journal of Computational Information Systems, 2014, 10 (24) :10655-10664
[24]   Collaborative task scheduling with new task arrival in cloud manufacturing using improved multi-population biogeography-based optimization [J].
Dai, Ziwei ;
Zhang, Zhiyong ;
Chen, Mingzhou .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 41 (02) :3849-3872
[25]   Task Scheduling Optimization Based on Firefly Algorithm in Storm [J].
Duan, Wen ;
Zhou, Liang .
PROCEEDINGS OF 2020 IEEE 10TH INTERNATIONAL CONFERENCE ON ELECTRONICS INFORMATION AND EMERGENCY COMMUNICATION (ICEIEC 2020), 2020, :150-154
[26]   Task scheduling for control system based on deep reinforcement learning [J].
Liu, Yuhao ;
Ni, Yuqing ;
Dong, Chang ;
Chen, Jun ;
Liu, Fei .
NEUROCOMPUTING, 2024, 610
[27]   An integrated optimization method to task scheduling and VM placement for green datacenters [J].
Liu, Hong ;
Zhou, Xuran ;
Gao, Kun ;
Ju, Yun .
SIMULATION MODELLING PRACTICE AND THEORY, 2024, 135
[28]   Processor allocation and task scheduling of matrix chain products on parallel system [J].
Lee, H ;
Kim, J ;
Hong, SJ ;
Lee, S .
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2003, 14 (04) :394-407
[29]   Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing [J].
Sreenivasulu, G. ;
Paramasivam, Ilango .
EVOLUTIONARY INTELLIGENCE, 2021, 14 (02) :1015-1022
[30]   Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing [J].
G. Sreenivasulu ;
Ilango Paramasivam .
Evolutionary Intelligence, 2021, 14 :1015-1022