000			03842nas|a2200409 a 4500
005			20260817161528.0
008			110110s2009 th uu|m rtt 0| a1eng d
035			_a.b12088353
099		9	_aAIT Thesis no.ISE-09-07
100	0		_aSuntaree Sae-huere
245	1	3	_aAn adaptive particle swarm optimization algorithm for a multicommodity distribution network design problem
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2009
300			_a54 p. : _bill.
490	1		_aThesis ; _vno. ISE-09-07
520			_aThis thesis studies a multicommodity distribution network design problem (MDNP) in the supply chain that involves locations of plants and distribution centers (DCs), and determining the best strategy to distribute the products in a distribution network. The goal of the model is to select the number, size and the location of plants and DCs in order to minimize the total relevant costs. To be more applicable in the industry, a model is formulated with the distance limitation constraint and the multi-capacity level availability for the facilities to supply each type of the products in each candidate plant and to store group products in each candidate DC. An adaptive Particle Swarm Optimization algorithm is applied to solve the problem.The parameters of particle swarm optimization to be adapted include inertia weight and acceleration constants. The algorithm is evaluated by using the benchmark problems provided by Vinaipanit (2006) and some additional randomly generated test problems. The solutions are compared with the solution from the commercial software package LINGO, GA (Vinaipanit), and GLNPSO without adaptive feature in order to verify the performance of the proposed algorithm. The results show that the proposed algorithm can solve the problem and performs well with the percentage of {u0BCC}{u0BEB}{u04A7}about 1.6 and obtain the better solution than GA in small and medium size. For large size problem, the solution is slightly inferior due to the condition of experiment that is the difference of population and iteration. Moreover, the quality of results from adaptive GLNPSO is better than non adaptive GLNPSO with the same parameters setting and significant level of 0.05.
500			_aSubmitted in partial fulfillment of the requirements for the degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering and Technology
502			_aThesis (M.Eng.) - Asian Institute of Technology, 2009
650		0	_aComputer algoritms
650		0	_aBusiness logistics _xMathematical models
700	0		_aVoratas Kachitvichyanukul, _eChairperson
700	1		_aHuynh, Trung Luong, _eExamination Committee
700	1		_aGong, Dah Chuan, _eExamination committee
710	2		_aRoyal Thai Goverment Fellowship, _eScholarship donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. ISE-09-07
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B03250
907			_a.b12088353 _bmnait _cu
902			_a240401
998			_b0 _c110110 _dm _ea _fu _g3
962			_a000:001:PDF:b1208835:000351:0:0:0:0:0:0 _tAbstract-AIT Thesis no.ISE-09-07 _vn
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