A two-phase genetic-immune algorithm with improved survival strategy of lifespan for flow-shop scheduling problems

Abstract

With the increase in manufacturing complexity, conventional production scheduling techniques for generating a reasonable manufacturing schedule have become ineffective. Therefore, applying efficient algorithm to solve the scheduling problems is essential for reducing the time budget. Genetic algorithms (GAs) is very effective in solving discrete combinatorial problems but they are frequently faced with a problem of early convergence. During the evolutionary processes, GAs are often trapped in a local optimum. In the literature, plenty of work has been investigated to introduce new methods for overcoming this essential problem of genetic algorithms. In this paper, a two-phase genetic-immune algorithm is developed to solve the flow-shop scheduling problems. The regular genetic algorithm is applied in the first-phase and when the processes are converged up to a pre-defined iteration then the artificial immune system (AIS) is introduced in the second phase. After the two-phase evolution process, the genetic immune algorithm (GIA) is applied to deal with different objective functions named antigen which will evoke the withstanding of antibodies. In the process of fighting, the antibodies will evolve till they can resist the antigen. An improved survival strategy of lifespan is proposed to extend the lifespan of the antibody so that can keep selected antibodies stay in system longer. Finally, the Two-phase genetic-immune algorithm (TPGIA) is tested on a set of flow-shop scheduling problems. The intensive experimental results show the effectiveness of the proposed approach when compared with other methods.