The Research Of Mixed Assembly Line Balancing Under The Networked Manufacturing Environment

Networked manufacturing is an advanced manufacturing mode which is generated in thecontext of knowledge economy and manufacturing globalization, and its main aim is to respondquickly to market demand and to improve the competitiveness of enterprises. The networkedmanufacturing involves multiple areas of machining, computer, information, network, electronicand automation.Mixed assembly line production system is an important part of the networked manufacturingplatform, its balance directly related to the degree of perfection of networked manufacturingsystem. Therefore, the system has become a key research of the assembly manufacturing. Throughthe balance research on the mixing assembly line production system, it can be achieved thatequipment utilization rate, production efficiency and market demand combine organically. At themeantime, a new perspective reflecting operational rules and complexity of the mixed assemblyline is raised, which reflects the advantage of efficient production of the mixed assembly line.Therefore, it’s very important to research the balance of mixed assembly line system on improvingthe networked manufacturing platform.Firstly, this thesls analyses the balancing problem of mixed assembly line and proposes twooptimization objectives: to present the balancing optimization model about the load among andinside workstations by using mathematical planning method and to design correspondingconstraints depending on the different optimization model. To combine with the two balancingmodels by adopting linear weighted method and to abtain the total evaluating index of mixedassembly production line finally.Secondly, the genetic algorithm is adoptd to solve objective functions because of thecomplication of balancing optimization model of mixed model assembly line in this thesls. Inaccordance with the characteristics of genetic algorithm, this part designs the generating method ofthe initial total group; according to the characteristics of the discrete event, this part adopts thesequence encoding and decoding rules; in order to improve search efficiency and avoid the endlessloop falling into the local optimal solution, the fitness scale conversion is adoptd to improve thecomputing method of fitness function; during the process of genetic manipulation, genetic operators are designd to revise and repair after completion of operation in order to avoid thephenomenon of constraint violation of precedence relation.Finally, in this thesls in order to further improve the solution results, Flexsim simulationsoftware is used to simulate the production process of automobile assembly line, the simulation ofvehicles which to be installed in the flowing process of different positions is completed, and sowell as the3D visualizations of the automotive assembly line. Meanwhile, the machining status ofvehicles which to be installed in the different positions are analysisd by taking advantage ofFlexsim statistical function. In the end, the correctness of the model is further validated though theanalysis of actual examples.