The Capacity Planning for Multiple Sites by Mathematical Programming – An Example from The Polarizer Production

• Main Authors: Ching-JuiLin, 林清瑞
• Other Authors: Tai-Yue Wang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/59367004367741876048

碩士 === 國立成功大學 === 工業與資訊管理學系專班 === 98 === With the blooming growth of TFT-LCD industry, more and more extensive product application, and incentive and support provided by governments (Taiwan, Korea, Japan, and China), TFT-LCD panel manufacturers continue with factory construction and production capacity expansion in order to keep competence, downsize costs, enlarge profits, and close to the requirements from customers. As a result, production planning becomes a problem for those producers. Polarizer is one of the main components of TFT-LCD panels, produced in the back-end of the overall TFT-LCD manufacturing process, and the whole industry encounters the same production planning problem among multiple factories as well. In the past, production planners scheme out the production plans based on experience without taking the optimization of aggregate planning into account. This thesis is establishing a production planning model to solve the multi-factory production allocation problem in polarizer industry. In the model, production balance and the bottlenecks of the resources in each factory are both taken into consideration. Among these, cost factor concerns manufacturing, transportation, and rework costs, and ordering demand concerns low season, high season, and ordering quantity. The primary purpose is to find a solution of production planning for each factory with the maximum profit, as well as providing adequate information to sales representatives and production planners for better order-taking and production planning. Meanwhile, sensitivity analysis is conducted against individual variable to determine the impact of the variables on profit. The conclusion of this research can be a reference to future study regarding production planning.