Research on immune genetic algorithm for solving the job-shop scheduling problem

To solve the job-shop scheduling problem more effectively, a method based on a novel scheduling algorithm named immune genetic algorithm (IGA) was proposed. In this study, the framework of IGA was presented via combining the immune theory and the genetic algorithm. The encoding scheme based on processes and the adaptive probabilities of crossover and mutation were adopted, while a modified precedence operation crossover was also proposed to improve the performance of the crossover operator. On the other hand, the “shortest processing time” principle was selected to be the vaccine of IGA and the design method of the immune operator was given at the same time. Finally, the performance of IGA for solving JSP was validated by applying the IGA to Muth and Thompson’s benchmark problems.