Optimization Study Of The Mixed-flow U-shaped Assembly Line Balance

U-shaped assembly line come from lean production assembly line. Compared with the traditional linear, U have better balance, and compact space is not easy to produce leisure. U-shaped assembly line to reduce workers walking paths, and at the same time due to material handling of waste to a minimum. U-shaped assembly line is a kind of flexible production, can adjust the production according to the demand of the beat. According to this feature, in the production of enterprises, u-shaped assembly line is used for assembling the product mix, the u-shaped mixed flow assembly line. As a result of the u-shaped assembly production efficiency by u-shaped mixed flow assembly line assembly process of many variable factors restricts directly, therefore, how to weaken or eliminate the u-shaped assembly line of variable factors, it is enterprises urgently need to solve the current problems.In this paper, based on the particularity to U mixed flow assembly line manufacturing, at the current enterprise widespread use of u-shaped mixed flow assembly line as the research object, and considering the preparation learning effect and preparation of multiple constraints cannot be ignored, such as preparation compatible repellent, open constraints, in the order of assembly process of principle, put forward the learning effects and the combination of a variety of constraints in actual production, and establish a mixed flow U nonlinear integer programming model, an assembly line for multiple optimization goal has carried on the comprehensive optimization, mixed flow assembly line is studied in the second class, third class comprehensive balance problem, and solved using improved genetic algorithm to optimize.Finally through the analysis about the present situation of production line of X enterprise, the model applied to the X in the enterprise to optimize validation, using the improved genetic algorithm, the improved weighted average load of each workstation equilibrium, rhythm,workstations of the assembly line homework time, build the model is verified by the applicability of the optimization model and improved genetic algorithm.