%0 Journal Article %A LOU Gaoxiang %A CAI Zongyan %A LIU Qingtao %T Application of new hybrid algorithm in mixed model assembly scheduling %D 2018 %R 10.3778/j.issn.1002-8331.1708-0304 %J Computer Engineering and Applications %P 254-259 %V 54 %N 16 %X This paper proposes a hybrid of Genetic Algorithm(GA) and Differential Evolution algorithm(DE) for actual mixed model assembly scheduling to address the production and inventory costs in mixed model assembly with buffer area. This hybrid algorithm comprehensively considers not only the reduction of production cost and the inventory at the buffer area but also the production order and quantity of each model of products by integrating the advantages of the GA in the effective processing of discrete variables and that of the DE in the effective processing of continuous variables. The computer simulation results demonstrate that the proposed hybrid algorithm has a higher convergence rate, stronger optimization ability, and better reliability in mixed model assembly scheduling than the traditional algorithms. This algorithm can also significantly improve the optimization and computational efficiency of multi-parameter and highly nonlinear problems. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1708-0304