A Genetic Algorithm for the Flexible Job-Shop Scheduling Problem

Scheduling for the flexible job-shop problem (FJSP) is very important. However, it is quite difficult to achieve an optimal solution to the FJSP with traditional optimization approaches. This paper examines the development and application of a genetic algorithm (GA) to the FJSP. A algorithm is proposed based on a basic genetic algorithm, an improved chromosome representation is represented, different strategies for crossover and mutation operator are adopted. The algorithm is tested on instances of 7 jobs and 7 machines. The results obtained from the computational study have shown that the proposed algorithm is a viable and effective approach for the FJSP.