A genetic algorithm approach to machine flexibility problems in an ion plating cell

For a long time, manufacturing industries have been concentrating on increasing productivity by increasing the size of the workforce, but this scenario has changed in last decade since the introduction of the term flexibility. Now, the manufacturers realize that flexibility of the machine environment can provide a better economic solution for improving productivity due to its quick response to the changing environment in the manufacturing industry. However, only very limited research on machine flexibility in the ion plating (IP) industry has been carried out and most of it has focused on product development and quality of coating. The aim of this paper is to determine the optimal level of machine flexibility in an ion plating cell (IPC) to improve the entire system performance. A machine loading sequencing (MLS) model based on a multi-objective genetic algorithm (GA) is developed and the case study of metal finishing company is discussed to validate the proposed model. Different levels of machine flexibility have been assigned to different machines to determine the optimal level to increase the overall system performance based on on-time delivery, quality of product and production cost. The results demonstrated that machine flexibility level in IPC should be zero under recent IP technology. However, when the IP technology is developed enough so that IP machine has the ability to produce different types of coating in high quality, machine flexibility should be introduced to enhance the overall system performance.