Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159272
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dc.contributor.authorLi, Yingyingen_US
dc.date.accessioned2022-06-12T12:24:53Z-
dc.date.available2022-06-12T12:24:53Z-
dc.date.issued2022-
dc.identifier.citationLi, Y. (2022). Crew scheduling problem for mass rapid transit systems. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159272en_US
dc.identifier.urihttps://hdl.handle.net/10356/159272-
dc.description.abstractThis 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 Problemen_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleCrew scheduling problem for mass rapid transit systemsen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorCheng Tee Hiangen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Computer Control and Automation)en_US
dc.contributor.supervisoremailETHCHENG@ntu.edu.sgen_US
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