A study on dynamic scheduling of multiobjective parallel machine - Example of Printed Circuit Board
碩士 === 輔仁大學 === 應用統計學研究所 === 92 === Manufacturing industry changes from that salesman take the product which has been made to sale to that industry manufacture the product which is needed after market research﹒The faster consumer’s demand varies ﹐the shorter the cycle time of product becomes﹒So the...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2004
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/30285623657622137058 |
| Summary: | 碩士 === 輔仁大學 === 應用統計學研究所 === 92 === Manufacturing industry changes from that salesman take the product which has been made to sale to that industry manufacture the product which is needed after market research﹒The faster consumer’s demand varies ﹐the shorter the cycle time of product becomes﹒So the deadline becomes more and more shorter﹒It is important for arrangement of work﹒The way manufacturing industry respond to this condition is changing faster as it can﹒It is not easy to chang﹐especially for order manufacture method and the industry which has complicated process﹒It is important for the type of Printed Circuit Board(PCB) to schedule﹒So this study is about product schedule the purpose of this study is minimization of line mix of total flow-time and tardiness about parallel machine dynamic scheduling﹒The indicators of the problem of product schedule are Total Flow Time﹑Total Tardiness﹑Number Of Tardy Jobs﹑Maximum Tardiness﹑Makespan﹒For Printed Circuit Board(PCB) ﹐Total Flow-Time and Tardiness important﹒lthough
The solutions of general scheduling problem are operations research and Heuristic﹒operations research need longer time for solving﹒Hand scheduling usually have the stress of time﹒There are a large amount of data to deal﹒Although Heuristic couldn’t solve the optimum﹐it can solve approximate optimum﹒It is faster than operations research and acceptable on business﹒So this study utilize Genetic Algorithm(GA)﹒This study is constracting a mathematical model for product scheduling﹒And then testing with actual data to prove the effectiveness about this study﹒The result is optimal﹒It is acceptable about the time of using Genetic Algorithm(GA) to solve﹒This study is testing with data of a company during﹒The testing result about approximate optimum is acceptable﹒It is carrespond to business demand and helpful for real task﹒
|
|---|