| Title |
Genetinio algoritmo parametrų tyrimas sprendžiant gamybos planavimo uždavinį / |
| Translation of Title |
Analysis of genetic algorithm parameters for manufacturing scheduling problem. |
| Authors |
Kravcovas, Viktoras |
| Full Text |
|
| Pages |
58 |
| Keywords [eng] |
genetic algorithm ; theory of constraints ; manufacturing |
| Abstract [eng] |
The main topic of this research paper is analysis of genetic algorithm parameters, when solving flexible flow shop (FFS) manufacturing scheduling problem. Scheduling, in general case, helps to solve the problems of efficient limited resources management. This research is based on approach of real manufacturing scheduling system and the data used in this paper is from currently working shop furniture factory. The main objective of this paper is to compare two different combinatorial optimisation methods and determine which of them is the most suitable for solving FFS problem. The research paper consist of five main topics: the analysis of scheduling tasks and the methods used to solve this problem; the analysis of genetic algorithm operators and their adaptation to FFS problem; the research of genetic algorithm and TOC heuristic when solving FFS problem; the conclusion of the research; the application of achieved conclusions and recommendations for further research. In the end author concludes that the best algorithm for solving FFS problem is genetic algorithm with mu+lambda selection method and swap mutation operator. The parameters estimate for three different data matrix sizes were almost constant. In distant future the application software used in this research will be updated for better representation of solution with some control mechanism of schedule progress and it will be used for scheduling real manufacturing proceses. |
| Type |
Master thesis |
| Language |
Lithuanian |
| Publication date |
2013 |
