THE USE OF SIMULATION WITH MACHINE LEARNING AND OPTIMIZATION FOR A DIGITAL TWIN- A CASE ON FORMULA 1 DSS

被引：3

作者：

Greasley, Andrew ^{[1
]}

Panchal, Gajanan ^{[2
]}

Samvedi, Avinash ^{[3
]}

机构：

[1] Aston Univ, Coll Engn & Phys Sci, Dept Engn Syst & Supply Chain Management, Birmingham B4 7ET, England

[2] Aston Univ, Coll Business & Social Sci, Dept Operat & Informat Management, Birmingham B4 7ET, England

[3] Natl Univ Singapore, Dept Ind Syst Engn & Management, 1 Engn Dr 2,Block E1A,06-25, Singapore 117576, Singapore

来源：

2022 WINTER SIMULATION CONFERENCE (WSC) | 2022年

关键词：

STRATEGIES; DESIGN;

D O I：

10.1109/WSC57314.2022.10015299

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The implementation of a digital twin presents a challenging environment for simulation. One challenge is the need for fast execution speed to maintain synchronization with the real system. When providing predictive outcomes, the complementary use of simulation with machine learning and optimization software may be employed to achieve this aim. The article investigates the use of simulation, machine learning and optimization in terms of providing a digital twin capability. The article presents a case on Formula1 or F1 competition, where a decision support system (DSS) framework is presented to explore a digital twin capability.

引用

页码：2198 / 2209

页数：12

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