Solving the two-objective shop scheduling problem in MTO manufacturing systems by a novel genetic algorithm

In this paper, a novel genetic algorithm (GA) is proposed to solve the two-objective shop scheduling problem in make-to-order (MTO) manufacturing systems. This algorithm can ensure that all jobs meet their deadlines; simultaneously, it can satisfy another performance goal which the enterprise pursues. Referring to the principle of population updating with survival of the fittest in traditional genetic algorithm and taking advantage of the idea of two sub-modules, the novel algorithm is controlled by the two nested closed-loops, and the strategy that feasible solutions are preferred while infeasible solutions are remade is employed to make the search forward. Finally the novel algorithm and the traditional algorithm are used to solve the two-objective hybrid flow-shop scheduling problem (HFSP) in MTO manufacturing systems. The result shows that the novel algorithm has an obvious advantage and good feasibility compared with the traditional algorithm. © (2011) Trans Tech Publications