A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem
| Autor: | Abderrazek Jemai, Rim Zarrouk, Imed Eddine Bennour |
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| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
education.field_of_study Optimization problem Computer science Population Particle swarm optimization 02 engineering and technology Job shop scheduling problem Scheduling (computing) 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing education Computer Science::Operating Systems Computer communication networks Algorithm |
| Zdroj: | Swarm Intelligence. 13:145-168 |
| ISSN: | 1935-3820 1935-3812 |
| Popis: | Particle swarm optimization is a population-based stochastic algorithm designed to solve difficult optimization problems, such as the flexible job shop scheduling problem. This problem consists of scheduling a set of operations on a set of machines while minimizing a certain objective function. This paper presents a two-level particle swarm optimization algorithm for the flexible job shop scheduling problem. The upper level handles the operations-to-machines mapping, while the lower level handles the ordering of operations on machines. A lower bound-checking strategy on the optimal objective function value is used to reduce the number of visited solutions and the number of objective function evaluations. The algorithm is benchmarked against existing state-of-the-art algorithms for the flexible job shop scheduling problem on a significant number of diverse benchmark problems. |
| Databáze: | OpenAIRE |
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