000			02117nam a22003133a 4500
001			UPMIN-00004780428
003			UPMIN
005			20221020172042.0
008			221020b |||||||| |||| 00| 0 eng d
040			_aDLC _cUPMin _dupmin
041			_aeng
090		0	_aLG993.5 2010 _bC6 M67
100			_aMorales, Francis Marie D.
245			_aDiscrete adaptation of the artificial bee colony algorithm applied to time-cost trade-off problem / _cFrancis Marie D. Morales
260			_c2010
300			_a76 leaves.
502			_aThesis (BS Computer Science) -- University of the Philippines Mindanao, 2010
520	3		_aTime cost optimization (TCO) may be define as a process to identify suitable construction activities for speeding up, and for deciding "by how much" so as to attain the best possible savings in both time and cost. It is generally realized that when project duration is compressed, the project will call for an increase in labor and more productive equipment, and require more demanding procurement and construction management, resulting to increase of cost. On the other hand, using fewer resources will result in extended duration of activities. In this papare, we have proposed a discrete adaptation of the artificial bee colony (ABC) algorithm for the time-cost trade-off problem. The ABC algorithm is a new metaheuristic approach inspired by intelligent foraging behavior of honeybee swarm.We have compared the performance of our discretely adapted ABC against three algorithms: Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) and genetic algorithm (GA). Computational results demonstrate the superiority of the discrete ABC over the three algorithms. It obtained better qualtiy solutions in shorter time.
650	1	7	_aTime-cost optimization
650	1	7	_aArtificial bee colony algorithm
650	1	7	_aTime-cost trade-off
650	1	7	_aParticle swarm optimization
650	1	7	_aGenetic Algorithm
650	1	7	_aAnt colony optimization
658			_aUndergraduate Thesis _cCMSC200, _2BSCS
905			_aFi
905			_aUP
942			_2lcc _cTHESIS
999			_c2450 _d2450