An effective genetic algorithm for the flexible job-shop scheduling problem

In this paper, we proposed an effective genetic algorithm for solving the flexible job-shop scheduling problem (FJSP) to minimize makespan time. In the proposed algorithm, Global Selection (GS) and Local Selection (LS) are designed to generate high-quality initial population in the initialization stage. An improved chromosome representation is used to conveniently represent a solution of the FJSP, and different strategies for crossover and mutation operator are adopted. Various benchmark data taken from literature are tested. Computational results prove the proposed genetic algorithm effective and efficient for solving flexible job-shop scheduling problem.

Research highlights
► Global Selection and Local Selection generate high-quality initial population.
► An improved chromosome representation is used to represents the solution of the FJSP.
► Different strategies for crossover and mutation operator are adopted.