Aiming at the problems of poor population diversity and convergence ability of the progeny of NSGA-II algorithm in dealing with shop floor scheduling optimization, an improved NSGA-II algorithm is proposed. The new algorithm mainly proposes the new improved adaptive crossover and mutation operators for the crossover. It compares the individual crowding degree with the population average crowding degree, and combines with the population iterative evolution process. The genetic probability is associated with the population individual and the population evolution iteration times. So it avoids blind guidance and improves the convergence speed of the population. The new algorithm proposes the new uniform evolution elite retention strategy. Through choosing the population individuals via the adaptive hierarchy, it solves the problem of the poor diversity of the population of the offspring. Finally, it regards “maximize the minimum delivery lead time” and “minimize the maximum ideal processing time deviation” as the objective function. In the light of the problem of shop floor scheduling, it uses the improved NSGA-II algorithm to carry out the simulation analysis of the actual project. And by comparing the results of the algorithm optimization before and after the improvement, the effectiveness of the algorithm is verified. Its value parameter applied to the actual production scheduling problem examination is also proved.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2006-0067