An empirical study on the tool capacity allocation system

碩士 === 南華大學 === 資訊管理學研究所 === 96 ===   The purpose of this study is to establish a capacity allocation decision support system for a wafer foundry factory. In today’s fierce competing world, one of the key factors for maintaining competitive advantage is to reach production efficiency. And this is ba...

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Bibliographic Details
Main Authors: Hong-jhan Lian, 練鴻展
Other Authors: Hsiang-yi Lee
Format: Others
Language:zh-TW
Published: 2008
Online Access:http://ndltd.ncl.edu.tw/handle/03659253686372977855
Description
Summary:碩士 === 南華大學 === 資訊管理學研究所 === 96 ===   The purpose of this study is to establish a capacity allocation decision support system for a wafer foundry factory. In today’s fierce competing world, one of the key factors for maintaining competitive advantage is to reach production efficiency. And this is based on the fully utilization of tool capacity. However, this is not an easy job since tool capacity varies as tool type varies and different products usually consume different tool capacity. A correct math programming model must be set up to gain the optimal allocation and computer software may be required. The challenge this study faced is that tool capacity changes as it is used in a single process or multiple process. That is, the capacity limitation is not fixed, it is 1.0 when a tool is assigned for one single process only and it is 0.9 when the tool is assigned for multiple processes. The study then adapts an integer programming model with two types of binary integer variables. The first one is to indicate that the tool is used in certain process or not. It becomes 1 when the tool is allocated or 0 when the tool is not allocated. Another binary integer is then used to indicate whether the tool is used in single process or cross-process used. The result is then used to determine the right capacity constrain. The system is developed via an Excel file with a Lingo kernel. The results indicate that the system could get the result within 1-2 min, while the manual way used in that factory took more than 10 min. The system also makes sure the output is optimized with the capacity allocation. This indicates that this system could be helpful for Industrial Engineer when making capacity planning.