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| 008 | 140402t2013 my da f m 000 0 eng d | ||
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_a201905131215 _byusri _c201404030757 _dFida _y201404021327 _zFida |
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| 040 | _aUMP | ||
| 090 | _aQA76.63 .T44 2013 rs Bc. | ||
| 100 | 1 | _aTeh, Yung Chuen | |
| 245 | 1 | 0 |
_aDynamic timetable generator using particle swarm optimization (PSO) method / _cTeh Yung Chuen |
| 260 |
_aKuantan, Pahang : _bUMP, _c2013 |
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| 300 |
_axiii, 42 p. : _bill. (some col.) ; _c30 cm. + _e1 CD-ROM |
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| 502 | _aProject paper (Bachelor of Computer Science (Software Engineering)) -- Universiti Malaysia Pahang - 2013 | ||
| 504 | _aBibliography : p. 37 | ||
| 520 | 3 | _aThis 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. | |
| 650 | 0 | _aLogic programming | |
| 650 | 0 | _aSwarm intelligence | |
| 999 |
_aVIRTUA40 _c4447 _d4453 |
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