Research on immune genetic algorithm for solving bi-objective scheduling problems subjected to special process constraint

The study presents a bi-objective scheduling model on parallel machines(BOSP), and proposes an immune genetic algorithm (VIGA) based on the vector group encoding method and the immune method. Compared with other scheduling problems on parallel machines, The BOSP is distinct for the following characteristics: (1) parallel machines are non-identical; (2) the sort of jobs processed on every machine can be restricted; (3) take minimizing the total tardiness penalty and minimizing the total completion time into account as a bi-objective problem. For VIGA, its three distinct characteristics are described as follows. Firstly, individuals are represented by a vector group, which can effectively reflect the virtual scheduling policy; Secondly, an immune operator is adopted and studied in order to guarantee diversity of the population; Finally, a local search algorithm is applied to improve quality of the population. Numerical experiments show that it is efficient, and can better overcome drawbacks of the genetic algorithm proposed in [J.Q. Gao and G.X. He, 2008]. A much better prospect of application can be optimistically expected.