Blockchain-Enabled Service Optimizations in Supply Chain Digital Twin

被引:30
|
作者
Gai, Keke [1 ,2 ]
Zhang, Yue [3 ]
Qiu, Meikang [4 ]
Thuraisingham, Bhavani [5 ]
机构
[1] Sch Cyberspace Sci & Technol, Beijing 100070, Peoples R China
[2] Beijing Inst Technol, Yangtze Delta Reg Acad, Beijing 100811, Zhejiang, Peoples R China
[3] Beijing Inst Technol, Sch Comp Sci & Technol, Beijing 100811, Peoples R China
[4] Dakota State Univ, Beacom Coll Comp & Cyber Sci, Madison, SD 57042 USA
[5] Univ Texas Dallas, Dept Comp Sci, Richardson, TX 75080 USA
基金
中国国家自然科学基金;
关键词
Digital twins; Supply chains; Digital twin; blockchain; supply chain management; consensus; optimization;
D O I
10.1109/TSC.2022.3192166
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Digital twin is considered an alternative for optimizing real-world performance within virtual context, which also applies to the optimization of Supply Chain Management (SCM). Blockchain, which facilitates data secure storage and trusted tracking, is deemed to be a proper assistant technology for achieving digital twin implementation. In this work, we propose a blockchain-based digital twin solution to reengineer SCM system, which promotes the digitization and intelligence of SCM to fit in massive service volumes in complex-intercrossed industry system. A strong-weak consensus mode is developed to achieve energy and time savings. We also design intelligent switch-based algorithms to generate time-saving consensus plans under energy constraints. Finally, we set up multiple experiments to compare our algorithm with three baseline algorithms, including Effective Iterative Greedy (EIG), Two Dimensional Genetic (TDG), and High-level Task Scheduling Dynamic Programming (HTSDP). Findings from evaluation demonstrate the potential of our proposed model. Specifically, our algorithm reduces time and energy consumption of EIG algorithm in consensus by 46.84% and 16.25%, respectively. Compared with TDG algorithm, consensus time and energy consumption of our algorithm are reduced by 50.05% and 48.46%. Our algorithm cuts down time spent of HTSDP algorithm in generating consensus plan by a factor of 9.88.
引用
收藏
页码:1673 / 1685
页数:13
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