The Research Of Balancing Optimization And Scheduling Of Mixed Model Assembly Line

With the market economy and the growing level of competition, firms are under constant pressure to produce an increased number of variations on a basic product, so they can attract every single customer. Due to the high cost to build and maintain an assembly line, the manufactures produce on model with different options on a single assembly line. A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar in product characteristics are assembled. Because of the line balancing and sequencing procedures, there is a great waste of time and resources of the assembly line. Therefore, the combination of technology innovation and management innovation to explore the methods of assembly line has great theoretical and practical significance.In this paper, we focus on the problem of line balancing and sequencing, apply the methods of Work Study to an assembly line, and present the models of sequencing. The main researches in this paper are as follows:(1) We apply the methods of Work Study to a certain hydraulic component company; Basing on the principle of ECRS (Eliminate, Combine, Rearrange, and Simple) and the line balancing theory, the production capability of assembly line are improved up and workshop condition is also improved.(2) Based on the line balancing, we present a novel sequencing model to minimize makespan, and adopt the method of lagrangian relaxation and subgradient to solve this problem.(3) We describe the multiple objective sequencing problems and provides its mathematical formulation considering three practically important objectives:minimizing makespan, keeping a constant rate of part usage and maximizing the sum of similarity based on summarizing the status of assembly flow line. At last, a case study is given to verify the feasibility and practicability of the model.The presented models are proved to be efficient in improving the production capability of assembly line. At the same time, we provide an optimal solution for decision-makers within the scope of the selection decisions as small as possible.