An improved genetic algorithm using opposition-based learning for flexible job-shop scheduling problem

Abstract

Aiming at the flexible job-shop scheduling problem, the mathematical model was established with the objective of minimizing the makespan, and an improved genetic algorithm using opposition-based learning was proposed. For the characteristics of flexible job-shop scheduling, a dual chains structure coding method was used to encode the chromosome. Population was initialized with a hybrid scheme. Genetic operations were conducted in population among two effective crossover methods and two mutation methods, which were proposed basis of context coding method. Lastly, case-studies based on some typical benchmark examples were carried out to evaluate the proposed algorithm. The experimental results show that these improvements allow the genetic algorithm to reach high quality solutions in very short time.