The Application And Research Of Mixed-model Assembly Lines Sequencing Based On Differential Evolution Algorithm

Scheduling problem plays an important role in improving the efficiency of the manufacturing system and reducing production costs; the core technology is to establish an effective scheduling model and its corresponding optimization algorithms. Mixed-model assembly lines (MMAL) are a type of production lines where a variety of products models similar to product characteristics are assembled in a just-in-time (JIT) production system. The MMAL sequencing problem is a branch of the scheduling problems. With strong sense of engineering application background and complexity of its solving optimization, numerous research efforts have been directed towards the development of heuristics algorithms as well as their subsequent implementation to solve MMAL. In this paper, MMAL sequencing problem is studied and the major works are as follows:Firstly, we review the optimization of MMAL sequencing problem in intelligent automobile manufacturing system. There is a set of criteria on which to judge sequences of product models in the center of two different general objectives: work overload objectives and JIT related objectives. Many real-word problems often require simultaneously optimize multiple competitive or conflicting objectives. The mixed-model assembly line sequencing problem has also been studied as a multi-objective problem.Secondly, we present a more rigorous description of the new optimization method differential evolution algorithm (DE) in the following. It details how DE works, how to use it and when it is appropriate. And then the recent research of DE is briefly introduced including search strategies, control parameters.Thirdly, based on study of the existing models and theories of MMAL sequencing, the experimental results on the application of solving MMAL sequencing problem validate its effectiveness and superiority. The implementation of the scheduling subsystem in MES makes great effort in executing the exact manufacturing planning.Finally, we give a summary of the paper and reveal the further research underway in developing more efficient hybrid DE global optimization method.