000			04224nas|a2200409 a 4500
005			20260817161525.0
008			110107s2009 th uu|m rtt 0| a1eng d
035			_a.b12088316
099		9	_aAIT Thesis no.ISE-09-04
100	0		_aDao Duc Cuong
245	1	3	_aAn adaptive hybrid algorithm for multi-mode resource-constrained project scheduling problems
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2009
300			_a92 p. : _bill.
490	1		_aThesis ; _vno. ISE-09-04
500			_aSubmitted in partial fulfillment of the requirements for the degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering Technology
520			_aThis thesis focuses on the multi execution modes resource-constrained project scheduling problem with the objective of minimizing the project makespan. There might be more than one operating mode to perform an activity; each mode requires different amount of resources and related time duration. Besides, there are two types of resources: renewable and non-renewable. Adaptive particle swarm optimization and genetic algorithm are integrated in an algorithm called APSO-GA to solve the MRCPSP. The major motivation of APSO-GA is to use PSO to find the best priority of activities while GA is used to search for the combination mode of the activities. A potential solution of MRCPSP is represented as a pair of particle and chromosome and an active schedule can be achieved by transforming this representation by serial schedule method. In adaptive PSO algorithm, the two parameters evolved in velocity updating mechanism 12,cc can be self-adaptive depending on three factors, including their old values, the degree of acceleration and the swarm response. At the beginning of the searching procedure, the cognitive learning term plays a more important role than the social learning term in effect to particle{u2018}s velocity. The effect of cognitive learning term is decreased while that of social learning term is increased through the PSO iteration. Genetic algorithm is put inside the PSO algorithm, i.e. in each PSO iteration, a GA loop is used to search for a better mode combination of activities. The GA loop is performed until the exploring process cannot find a better combination of mode for current activities priority arrangement. Moreover, three difference types of GA crossover variants are applied to enhance the quality of searching procedure. The performance of the APSO-GA algorithm is investigated on standard instance sets. Experiment results show that the proposed adaptive PSO algorithm has proved the advantages over the non-adaptive one and the best GA crossover types is uniform crossover. The makespan results and computational time results of the proposed algorithm are also very competitive in comparing with others heuristics previously published.
502			_aThesis (M.Eng.) - Asian Institute of Technology, 2009
650		0	_aProduction scheduling
650		0	_aComputer algorithms
700	0		_aVoratas Kachitvichyanukul, _eChairperson
700	1		_aGong, Dah-Chuan, _eExamination committee
700	1		_aHuynh, Trung Luong, _eExamination Committee
710	2		_aPetroVietnam, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. ISE-09-04
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B03248
907			_a.b12088316 _bmnait _cu
902			_a240401
998			_b0 _c110107 _dm _ea _fu _g3
962			_a000:001:PDF:b1208831:000348:0:0:0:0:0:0 _tAbstract-AIT Thesis no.ISE-09-04 _vn
945			_lmnait
945			_lmnarc
942			_c22
942			_c40
909			_aBarcode : 30050120700934 _bCREATED : 2014-03-20 _cRECORD # : i12774650 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050160030689 _bCREATED : 2016-03-16 _cRECORD # : i12892282 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c1607 _d1607