A genetic algorithm and simulated annealing for determining the number of kanbans and a withdrawal lot size | |
| Author | I Gede Agus Widyadana |
| Call Number | AIT Thesis no.ISE-00-32 |
| Subject(s) | Production planning Genetic algorithms |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering, School of Advanced Technologies |
| Publisher | Asian Institute of Technology |
| Series Statement | Thesis ; no. ISE-00-32 |
| Abstract | This study proposes metaheuristic methods for determining the number of kanbans and a withdrawal lot size. A dual card kanban system is observed, to consist of production kanban and withdrawal kanban cards. The approach of this study is different from previous researches because it includes the transportation cost between workstations. This cost occurs when workstations are not in the same area. As a consequence, there is trade off between the transportation cost and inventory holding cost, and, therefore, the withdrawal lot size and frequency of transfer between workstations is important and should be considered. The problem has been modeled into an integer programming (IP), but it is time consuming to get the problem solved. Metaheuristic methods, namely, Genetic Algorithm (GA) and a combination of Genetic Algorithm and Simulated Annealing (GASA) were developed to solve this problem. Three cases with different production systems and product structures were built for experiments. Experiments were designed to see the effect of Genetic Algorithm and Simulated Annealing parameters and to find their best values. Parameters of Genetic Algorithm to be considered are the number of populations, crossover probability and mutation probability. The parameters of Simulated Annealing are initial temperature multiplier and cooling rate. The performance of GA and GASA is measured by comparing its results with the optimum ones obtained from the IP. The result shows that for all test problem, GASA provides better or equal results compared with those of GA. Both GA and GASA, in general, outperformed the optimization method in term of a significant run time improvement while provided the near optimal solutions |
| Year | 2000 |
| Corresponding Series Added Entry | Asian Institute of Technology. Thesis ; no. ISE-00-32 |
| Type | Thesis |
| School | School of Advanced Technologies (SAT) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Industrial Systems Engineering (ISE) |
| Chairperson(s) | Anulark Pinnoi; |
| Examination Committee(s) | Nagarur, Nagendra N.;Voratas Kachitvichyanukul; |
| Scholarship Donor(s) | Petra Christian University;Indonesia; |
| Degree | Thesis (M.Eng.) - Asian Institute of Technology, 2000 |