An adaptive particle swarm optimization algorithm for a multicommodity distribution network design | |
| Author | Suntaree Sae-huere |
| Call Number | AIT Thesis no.ISE-09-07 |
| Subject(s) | Computer algoritms Business logistics--Mathematical models |
| Note | Submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Industrial and Manufacturing Engineering, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ISE-09-07 |
| Abstract | This 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 ௌ௫ҧabout 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. |
| Year | 2009 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ISE-09-07 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Voratas Kachitvichyanukul |
| Examination Committee(s) | Huynh Trung Luong;Gong, Dah Chuan |
| Scholarship Donor(s) | Royal Thai Goverment Fellowship |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2009 |