Artificial fish swarm algorithm for job shop scheduling problem

• Published in: 2015 3rd International Conference on Information and Communication Technology (ICoICT) pp. 437 - 443
• Main Authors: Pythaloka, Dyah, Wibowo, Agung Toto, Sulistiyo, Mahmud Dwi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2015
• Subjects: Artificial Fish Swarm Algorithm; job shop; Job shop scheduling; makespan; Marine animals; Optimal scheduling; optimization; Particle swarm optimization; Schedules; scheduling; Visualization
• DOI: 10.1109/ICoICT.2015.7231465

One of the most difficult combinatorial optimization problems in recent studies is job shop scheduling. Job shop scheduling which also holds the key to the company's profitability is a crucial problem faced by many manufacturing companies. Well-structured scheduling has the potential to reduce operating costs and increase profits. Artificial Fish Swarm Algorithm (AFSA) is one of optimization algorithms to solve combinatorial problems. This paper talks about the implementation of AFSA in job shop scheduling cases to produce an optimal solution, containing a minimum completion total time (makespan) of the entire job. The results showed that the AFSA which is designed for job shop scheduling problem optimization is able to provide solutions with the best efficiency value ever achieved was 75%. This figure is still considered unsatisfactory based on the makespan resulted. Nevertheless, the AFSA ability in the searching process for solutions is quite good considering that level of efficiency is achieved by only 10000 artificial fishes around 100 generations within 3,72e+41 solution spaces.