Applying two-phase adaptive genetic algorithm to solve multi-model assembly line balancing problems in TFT–LCD module process

Abstract

Abstract The module process is labor-intensive in the thin film transistor–liquid crystal display (TFT–LCD) industry because of the difficulty in applying automation to this process as compared with array, color filter, and cell processes. The module process is also considered a multi-model assembly line, which means several models from a basic product family are manufactured simultaneously. Therefore, a module process with integrated arrangement and line-balancing can reduce labor requirements and increase production efficiency. This research considered several practical characteristics of the TFT–LCD module process, including multi-skilled workers and operator efficiency, to address the resource-constrained multi-model assembly line balancing problem. A novel mathematical programming model is proposed to obtain the optimal allocation of tasks, workers, machines, and workstations. A heuristic two-phase approach based on the adaptive genetic algorithm is developed to address this NP-hard problem. Data from the TFT–LCD module factories in Taiwan are applied to evaluate the performance of the proposed approach based on the design of experiments and response surface method. This study integrated theoretical research and practical applications for the assembly line balancing problem in the TFT–LCD module process. Hence, from the results, the production efficiency can be improved and the cost can be reduced, which can enhance the global competitiveness of TFT–LCD manufacturers.