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Dynamic timetable generator using particle swarm optimization (PSO) method / Teh Yung Chuen

By:

Teh, Yung Chuen

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2013Description: xiii, 42 p. : ill. (some col.) ; 30 cm. + 1 CD-ROMISBN:

THE0001735(Local)

Subject(s):

Dissertation note: Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2013 Abstract: 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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Holdings ( 2 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	CD 7604 \| QA76.63 .T44 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078851
Final Year Report	UMPLIB PEKAN	QA76.63 .T44 2013 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000078850

Project paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2013

Bibliography : p. 37

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.