| Author | Thitiwan Sutanon |
| Call Number | AIT Thesis no.ISE-07-09 |
| Subject(s) | Business logistics--Mathematical models
|
| Note | A thesis submitted in partial fulfillment of the requirements for the
degree of Master of Engineering in
Industrial Engineering & Management, School of Engineering and Technology |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ISE-07-09 |
| Abstract | This thesis presents a multi-objective location-allocation problem. The Goals of the model are the selection of the best location for potential plants and distribution centers by considering the limited number of opening plants, and allocation the products through logistics and supply chain system. The two objectives are to minimize total fixed cost and transportation cost, and to minimize unsatisfied customer's demands by considering as penalty cost. Besides the additional feature which is extended from single objective model, this study proposes the different allocating strategy and still considers the two-stage capacitated facility location problem with availability of multiple capacity levels for plants and distribution centers and multiple products to make the problem more realistic in the real world. The genetic algorithm with weight sum approach is applied to solve the problem. A genetic algorithm (GA) is the metaheuristic algorithm which is developed to solve the difficult optimization problem. This algorithm is designed and implemented with the class library from the Genetic Algorithm Library (GALib). The real number encoding method is used in the proposed algorithm. The operators and parameters of the GA are examined to find the best combination specified for each problem group. Different size of test problems are randomly generated and solved by proposed algorithm. The solutions show that the proposed algorithm performs well in the case of multi¬objective to consider the minimized total relevant cost before making decision to open the new plants and distribution centers to satisfy the remaining customer demands. This multi¬objective is useful for the real situation. GA can be easily implemented to consider various scenarios for this specific problem and the design of experiments is appropriate approach to integrate the GA operators and GA parameters in order to obtain the best combination among various factors |
| Year | 2007 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ISE-07-09 |
| 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;Pisut Koomsap; |
| Scholarship Donor(s) | RTG Fellowship; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2007 |