Immune genetic algorithm for multi-objective flexible job-shop scheduling problem

In this paper, the multi-objective flexible job-shop scheduling problem (FJSP) is studied and formulated in a mixed integer programming model. Combining the artificial immune mechanism and genetic algorithm, an immune genetic algorithm is proposed to solve the FJSP. It can generate excellent individuals based on coding and heuristic rules in the initial population and combine the Pareto-optimality and random to deal with the multiple objectives of the FJSP. The immune mechanism is applied to increase the chaotic sequence search feature in order to improve the diversity in process of genetic evolution which can generate better solution. Numerical experiments demonstrate the effectiveness and efficiency of the algorithm.