Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules
Due to the rise of Industry 4.0, flexible manufacturing systems and automation solutions with machine learning solvers have been widely adopted by manufacturers to provide flexibility in the assembly line. With the operation of Automated Guided Vehicles (AGV) based on the Discrete Event System...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | |
| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2022
|
| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/158566 |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| id |
sg-ntu-dr.10356-158566 |
|---|---|
| record_format |
dspace |
| spelling |
sg-ntu-dr.10356-1585662023-07-07T18:56:57Z Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules Teo, Jia Ling Su Rong School of Electrical and Electronic Engineering RSu@ntu.edu.sg Engineering::Electrical and electronic engineering Due to the rise of Industry 4.0, flexible manufacturing systems and automation solutions with machine learning solvers have been widely adopted by manufacturers to provide flexibility in the assembly line. With the operation of Automated Guided Vehicles (AGV) based on the Discrete Event System (DES) framework in a flexible manufacturing system, route optimization techniques have been used to improve its scheduling performance. However, due to the complexity Vehicle Routing Problem (VRP), several constraints under given conditions have to be considered to reach an optimal solution. By considering the various constraints in VRP, an analysis of the AGV system can be done to improve efficiency. In this paper, we will discuss and experiment with the application of control theories and machine learning techniques to optimize logistic transportation for an AGV system using Google Optimization Tools (OR-Tools). Visualization of AGV routing in the assembly line will be conducted using a 3D simulation program, Visual Components. With the visualization, OR-Tools with simple machine learning techniques will account for the constraints to strategize an optimal route for AGV. Keywords: Machine Learning, Automated Guided Vehicle (AGV), Discrete Event System (DES), Vehicle Routing Problem (VRP), Google Optimization Tools (OR-Tools), Visual Components Bachelor of Engineering (Electrical and Electronic Engineering) 2022-06-04T11:12:18Z 2022-06-04T11:12:18Z 2022 Final Year Project (FYP) Teo, J. L. (2022). Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158566 https://hdl.handle.net/10356/158566 en A1131-211 application/pdf Nanyang Technological University |
| institution |
Nanyang Technological University |
| building |
NTU Library |
| continent |
Asia |
| country |
Singapore Singapore |
| content_provider |
NTU Library |
| collection |
DR-NTU |
| language |
English |
| topic |
Engineering::Electrical and electronic engineering |
| spellingShingle |
Engineering::Electrical and electronic engineering Teo, Jia Ling Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| description |
Due to the rise of Industry 4.0, flexible manufacturing systems and automation solutions with
machine learning solvers have been widely adopted by manufacturers to provide flexibility in the
assembly line. With the operation of Automated Guided Vehicles (AGV) based on the Discrete
Event System (DES) framework in a flexible manufacturing system, route optimization techniques
have been used to improve its scheduling performance. However, due to the complexity Vehicle
Routing Problem (VRP), several constraints under given conditions have to be considered to reach
an optimal solution. By considering the various constraints in VRP, an analysis of the AGV system
can be done to improve efficiency.
In this paper, we will discuss and experiment with the application of control theories and machine
learning techniques to optimize logistic transportation for an AGV system using Google
Optimization Tools (OR-Tools). Visualization of AGV routing in the assembly line will be
conducted using a 3D simulation program, Visual Components. With the visualization, OR-Tools
with simple machine learning techniques will account for the constraints to strategize an optimal
route for AGV.
Keywords: Machine Learning, Automated Guided Vehicle (AGV), Discrete Event System
(DES), Vehicle Routing Problem (VRP), Google Optimization Tools (OR-Tools), Visual
Components |
| author2 |
Su Rong |
| author_facet |
Su Rong Teo, Jia Ling |
| format |
Final Year Project |
| author |
Teo, Jia Ling |
| author_sort |
Teo, Jia Ling |
| title |
Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| title_short |
Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| title_full |
Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| title_fullStr |
Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| title_full_unstemmed |
Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| title_sort |
developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules |
| publisher |
Nanyang Technological University |
| publishDate |
2022 |
| url |
https://hdl.handle.net/10356/158566 |
| _version_ |
1772827806930042880 |