Study On Optimization Of Mixed Model Assembly Scheduling Problem Based On Improved Cuckoo Search Algorithm

As the mode of production is gradually developed from the traditional single variety, large quantities to multi-varieties and small batch, the requirement of flexibility is increasingly high. In this thesis, through a comprehensive analysis of the production mode of the assembly workshop of F automobile company, aiming at the order of production order, using a combination of theoretical analysis and practical research methods to comprehensive analysis and work out the material flat standard,production load balance and product similarity of the mixed model assembly scheduling problem.The main research contents of this thesis are:(1) Aiming at the cuckoo algorithm slow convergence speed and low optimization accuracy, based on the introduction of the adaptive step size factor and the differential algorithm selection and crossover idea, an improved cuckoo search algorithm is proposed. This algorithm improves the convergence speed and the precision of the basic cuckoo search algorithm, and has certain advantages in solving the function optimization problem and the combinatorial optimization problem.(2) By analyzing the scheduling problem of mixed flow assembly in the assembly shop of F automobile enterprises, the objective function of leveling, production load balance and product similarity is proposed. Taking an example of F automobile assembly workshop production, the improved hybrid cuckoo search algorithm is used to solve the each target, with the purpose of improving the production of material level and production efficiency, reducing the number of stop line and change.(3)Three production scheduling problems consist of material level, production load balance and product similarity, according to the relative importance, a multi-objective mixed flow assembly sequencing problem is constructed. A algorithm based on dynamic detection probability and inertia weight cuckoo search algorithm is proposed for solving the model. The results of the experiment are considered as the level of material, the balance of production load and the similarity of products. It shows that the algorithm is feasible and effective to solve the multi-objective model of mixed flow assembly sequencing.