Dynamic timetable generator using particle swarm optimization (PSO) method

This paper addresses the usage of Particle Swarm Optimization (PSO) in generating a timetable which the selection of driver and vehicle are based on the concept of PSO. The objectives are simultaneously considered as follow: 1) minimizing the cycle time, 2) regenerate the timetable. Searching for an...

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Main Author: Chuen, Teh Yung
Format: Undergraduates Project Papers
Language:English
Published: 2013
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/7241/1/Dynamic%20timetable%20generator%20using%20particle%20swarm%20optimization%20%28PSO%29%20method.pdf
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author Chuen, Teh Yung
author_facet Chuen, Teh Yung
author_sort Chuen, Teh Yung
collection UMP
description This paper addresses the usage of Particle Swarm Optimization (PSO) in generating a timetable which the selection of driver and vehicle are based on the concept of PSO. The objectives are simultaneously considered as follow: 1) minimizing the cycle time, 2) regenerate the timetable. Searching for an optimal solution in such of large sized population will be time consuming and thus by presenting the PSO method is able to select the appropriate driver and vehicle with a shorter period. The timetable that is generated will be more appropriate as regenerating function can handle emergency such as breakdown of vehicle. Besides, during the generating of timetable, it also considers constraints which make the task more challenging. The chosen particle during implementing the PSO method should be chosen with fitness nearest to fifty in this system. Thus, the timetable for transport schedule system can be arranged without clashing of driver or vehicle.
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spelling UMPir72412023-09-07T01:49:03Z http://umpir.ump.edu.my/id/eprint/7241/ Dynamic timetable generator using particle swarm optimization (PSO) method Chuen, Teh Yung QA Mathematics This paper addresses the usage of Particle Swarm Optimization (PSO) in generating a timetable which the selection of driver and vehicle are based on the concept of PSO. The objectives are simultaneously considered as follow: 1) minimizing the cycle time, 2) regenerate the timetable. Searching for an optimal solution in such of large sized population will be time consuming and thus by presenting the PSO method is able to select the appropriate driver and vehicle with a shorter period. The timetable that is generated will be more appropriate as regenerating function can handle emergency such as breakdown of vehicle. Besides, during the generating of timetable, it also considers constraints which make the task more challenging. The chosen particle during implementing the PSO method should be chosen with fitness nearest to fifty in this system. Thus, the timetable for transport schedule system can be arranged without clashing of driver or vehicle. 2013 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/7241/1/Dynamic%20timetable%20generator%20using%20particle%20swarm%20optimization%20%28PSO%29%20method.pdf Chuen, Teh Yung (2013) Dynamic timetable generator using particle swarm optimization (PSO) method. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang.
spellingShingle QA Mathematics
Chuen, Teh Yung
Dynamic timetable generator using particle swarm optimization (PSO) method
title Dynamic timetable generator using particle swarm optimization (PSO) method
title_full Dynamic timetable generator using particle swarm optimization (PSO) method
title_fullStr Dynamic timetable generator using particle swarm optimization (PSO) method
title_full_unstemmed Dynamic timetable generator using particle swarm optimization (PSO) method
title_short Dynamic timetable generator using particle swarm optimization (PSO) method
title_sort dynamic timetable generator using particle swarm optimization pso method
topic QA Mathematics
url http://umpir.ump.edu.my/id/eprint/7241/1/Dynamic%20timetable%20generator%20using%20particle%20swarm%20optimization%20%28PSO%29%20method.pdf
work_keys_str_mv AT chuentehyung dynamictimetablegeneratorusingparticleswarmoptimizationpsomethod