Syarif, Admi and Wamiliana, Wamiliana and Lumbanraja, Favorisen R and Gen, Mitsuo (2019) Study on Genetic Algorithm (GA) Approaches for Solving Flow Shop Scheduling Problem (FSSP). In: The 5TH International Conference on Science, Technology and Interdisciplinary Research (IC-STAR) 2019, 23-25 September 2019, Bandar Lampung. (In Press)
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Abstract
The scheduling problem is known as one of the well-known optimization problems. It occurs in many situations of our daily-life applications, especially in industrial fields. One class of scheduling problems is called Flow Shop Scheduling Problem (FSSP). It belongs to the class of NP-complete problem. During the last decades, researches on exploring more accurate and efficient heuristic methods to solve hard optimization problems have taken considerable attention of researchers. Among them, GA has been one of the powerful and widely used algorithms. In this paper, we present two GA approaches to solve FSSP. Our main objective is to investigate the effectiveness and the efficiency of GA based on different variations of the chromosome representation, referred to as the job-based GA (jb-GA) and machine-based GA (mb-GA). We conducted numerical experiments using standard test problems (Benchmark test problems). We also compare the results with those given by another heuristic algorithm (NBH Algorithm) and the optimal solutions reported in the literature. Those demonstrate the jb-GA is more effective and efficient almost all of the time. The current limitation of this approach, like many other heuristic methods, is that it still sometimes gives the near-optimal solutions.
| Item Type: | Conference or Workshop Item (Speech) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA) > Prodi Ilmu Komputer |
| Depositing User: | DR Admi Syarif |
| Date Deposited: | 03 Sep 2020 07:23 |
| Last Modified: | 03 Sep 2020 07:23 |
| URI: | http://repository.lppm.unila.ac.id/id/eprint/23874 |
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Study on Genetic Algorithm (GA) Approaches for Solving Flow Shop Scheduling Problem (FSSP). (deposited 15 Nov 2019 10:33)
- Study on Genetic Algorithm (GA) Approaches for Solving Flow Shop Scheduling Problem (FSSP). (deposited 03 Sep 2020 07:23) [Currently Displayed]
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