Optimization algorithms with adaptive learning for logistic planning

With the rise in popularity of Artificial Intelligence (AI) over the years, it has become more important than ever to capitalise on this interest to not only educate those who are interested, but also to inspire them to develop this interest into a skill. However, without knowledge of coding, it is...

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Main Author: Lee, Yan Hui
Other Authors: Meng-Hiot Lim
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/145233
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1452332023-07-07T18:10:30Z Optimization algorithms with adaptive learning for logistic planning Lee, Yan Hui Meng-Hiot Lim School of Electrical and Electronic Engineering EMHLIM@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems With the rise in popularity of Artificial Intelligence (AI) over the years, it has become more important than ever to capitalise on this interest to not only educate those who are interested, but also to inspire them to develop this interest into a skill. However, without knowledge of coding, it is difficult for people to learn about AI effectively. This project aims to address this issue by aiming to solve a popular computational problem called the Traveling Salesman Problem (TSP), which is an abstract representation of general logistic planning issues. By introducing a holistic package, which includes building hardware (Arduino based, wirelessly-controlled vehicle) and coding software for beginners, and by choosing materials which are easily available and accessible, both online and offline, it ensures that the barrier to entry is kept low to maximise the interest of learners. At the end of the program, learners would get to demonstrate their knowledge by showcasing their vehicle and code to solve a logistic planning scenario. Bachelor of Engineering (Electrical and Electronic Engineering) 2020-12-15T07:06:01Z 2020-12-15T07:06:01Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/145233 en A2324-192 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::Computer hardware, software and systems
spellingShingle Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Lee, Yan Hui
Optimization algorithms with adaptive learning for logistic planning
description With the rise in popularity of Artificial Intelligence (AI) over the years, it has become more important than ever to capitalise on this interest to not only educate those who are interested, but also to inspire them to develop this interest into a skill. However, without knowledge of coding, it is difficult for people to learn about AI effectively. This project aims to address this issue by aiming to solve a popular computational problem called the Traveling Salesman Problem (TSP), which is an abstract representation of general logistic planning issues. By introducing a holistic package, which includes building hardware (Arduino based, wirelessly-controlled vehicle) and coding software for beginners, and by choosing materials which are easily available and accessible, both online and offline, it ensures that the barrier to entry is kept low to maximise the interest of learners. At the end of the program, learners would get to demonstrate their knowledge by showcasing their vehicle and code to solve a logistic planning scenario.
author2 Meng-Hiot Lim
author_facet Meng-Hiot Lim
Lee, Yan Hui
format Final Year Project
author Lee, Yan Hui
author_sort Lee, Yan Hui
title Optimization algorithms with adaptive learning for logistic planning
title_short Optimization algorithms with adaptive learning for logistic planning
title_full Optimization algorithms with adaptive learning for logistic planning
title_fullStr Optimization algorithms with adaptive learning for logistic planning
title_full_unstemmed Optimization algorithms with adaptive learning for logistic planning
title_sort optimization algorithms with adaptive learning for logistic planning
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/145233
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