Adaptive Genetic Algorithm In The Application Of Mixed Flow Assembly Line Balancing

Mixed model assembly line is widely used to produce different models by arranging the tasks and production sequences reasonably on the condition of the facilities remain unchanged. The mixed model assembly line can fulfill the advantages of high efficiency of mass and standardization of simple assembly line, reduce the production cost and lead-time, maximize the potential of the assembly line, meet the customized demands and services of customers, increase the adaptability to the market. Line balancing is one of the most important problems in production planning and operation management, which involves productivity, equipment effectiveness, market demand and so on, will largely influence the production efficiency, quality and employee morale. Therefore, the intensive study of line balancing and optimization method of mixed model assembly line to increase the effectiveness and efficiency has an important practical significance.In this thesis, the method and theory of Work Measurement and Genetic Algorithm were applied to build the mathematical model of the multi-product mixed model assembly line balancing and workstations assignment and establish the solving methods. The mathematical model and solving method have an important theoretical significance and practical reference value for discrete manufacturing enterprises.Firstly, this thesis showed the background of assembly line balancing and reviewed several keys issues in research and analysis of mixed model assembly line, then the mathematical model was established. Secondly, to handle the complexity of the large scale model, the solving method used Genetic Algorithm was proposed. The original GA was improved based on the following aspects:(1) the production of initial population was studied,(2) the special method of coding/encoding was designed,(3) adaptive technology was used to modified the genetic operations of crossover and mutation. Finally, the case of the assembly line of teaching experimental equipment was given. The parameters of GA were adjusted according to the practical problem; problems of line balancing and tasks assignment were optimized. The case demonstrated that the proposed Adaptive Genetic Algorithm could solve the problem of tasks and workstations assignment, provide technical assistance to design and optimization of mixed model assembly line.