Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158566
Title: Developing a machine learning-based drag-and-play system for the automatic synthesis of optimal and correct-by-construction schedules
Authors: Teo, Jia Ling
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: 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
Project: A1131-211
Abstract: 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
URI: https://hdl.handle.net/10356/158566
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
FYP_U1822376D.pdf
  Restricted Access
1.6 MBAdobe PDFView/Open

Page view(s)

143
Updated on May 24, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.