Crew scheduling problem for mass rapid transit systems

This dissertation makes a detailed analysis of the Mass Rapid Transit (MRT) crew scheduling problem after summarizing the relevant literature. It introduces the relevant concepts and contents in crew scheduling. The crew scheduling problem has many complex constraints and it is a large-scale problem...

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Main Author: Li, Yingying
Other Authors: Cheng Tee Hiang
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/159272
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1592722023-07-04T17:51:56Z Crew scheduling problem for mass rapid transit systems Li, Yingying Cheng Tee Hiang School of Electrical and Electronic Engineering ETHCHENG@ntu.edu.sg Engineering::Electrical and electronic engineering::Computer hardware, software and systems This dissertation makes a detailed analysis of the Mass Rapid Transit (MRT) crew scheduling problem after summarizing the relevant literature. It introduces the relevant concepts and contents in crew scheduling. The crew scheduling problem has many complex constraints and it is a large-scale problem that needs a complex solution process. Based on the time and location constraints, an optimization model with the minimum cost as the optimization objective is constructed. The principles, characteristics, and steps of the ant colony algorithm are introduced. The improved algorithm provides more possibilities for the ant colony to search for the path and effectively avoid the algorithm from facing the local optimal state by using the heuristic information calculated from real-time information. Finally, the improved algorithm is used to solve the crew scheduling model of a real case. By comparing the crew scheduling scheme obtained with that in actual operation and that obtained by the genetic algorithm, relevant conclusions are drawn through analysis, thereby verifying the effectiveness of the model and algorithm. Keywords: Crew Scheduling Problem; Ant Colony Algorithm; Genetic Algorithm; Combinatorial Optimization Problem Master of Science (Computer Control and Automation) 2022-06-12T12:24:53Z 2022-06-12T12:24:53Z 2022 Thesis-Master by Coursework Li, Y. (2022). Crew scheduling problem for mass rapid transit systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159272 https://hdl.handle.net/10356/159272 en 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
Li, Yingying
Crew scheduling problem for mass rapid transit systems
description This dissertation makes a detailed analysis of the Mass Rapid Transit (MRT) crew scheduling problem after summarizing the relevant literature. It introduces the relevant concepts and contents in crew scheduling. The crew scheduling problem has many complex constraints and it is a large-scale problem that needs a complex solution process. Based on the time and location constraints, an optimization model with the minimum cost as the optimization objective is constructed. The principles, characteristics, and steps of the ant colony algorithm are introduced. The improved algorithm provides more possibilities for the ant colony to search for the path and effectively avoid the algorithm from facing the local optimal state by using the heuristic information calculated from real-time information. Finally, the improved algorithm is used to solve the crew scheduling model of a real case. By comparing the crew scheduling scheme obtained with that in actual operation and that obtained by the genetic algorithm, relevant conclusions are drawn through analysis, thereby verifying the effectiveness of the model and algorithm. Keywords: Crew Scheduling Problem; Ant Colony Algorithm; Genetic Algorithm; Combinatorial Optimization Problem
author2 Cheng Tee Hiang
author_facet Cheng Tee Hiang
Li, Yingying
format Thesis-Master by Coursework
author Li, Yingying
author_sort Li, Yingying
title Crew scheduling problem for mass rapid transit systems
title_short Crew scheduling problem for mass rapid transit systems
title_full Crew scheduling problem for mass rapid transit systems
title_fullStr Crew scheduling problem for mass rapid transit systems
title_full_unstemmed Crew scheduling problem for mass rapid transit systems
title_sort crew scheduling problem for mass rapid transit systems
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/159272
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