English  |  正體中文  |  简体中文  |  Items with full text/Total items : 28588/40619
Visitors : 4190342      Online Users : 47
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/14056

Title: 改良式遺傳演算法於零工式生產排程系統之應用
Application of Modified Genetic Algorithms on Job-Shop Scheduling Problems
Authors: Yi-Yuan Li
李宜原
Contributors: NTOU:Department of Systems Engineering and Naval Architecture
國立臺灣海洋大學:系統工程暨造船學系
Keywords: 零工式生產排程系統;遺傳演算法;改良式遺傳演算法;交配率;突變率;族群數
Job-shop scheduling problems;genetic algorithm;modified genetic algorithm;crossover rate;mutation rate;population size
Date: 2003
Issue Date: 2011-06-30T07:33:07Z
Abstract: 摘 要 近年來,啟發式演算法已逐漸成為解決組合最佳化中非多項式時間可解問題(NP-hard)的主流。而在這些組合最佳化問題中,零工式生產排程系統問題在生產管理方面是個相當重視的問題。本文主要針對遺傳演算法探討其於零工式生產排程系統問題的應用,提出改良式遺傳演算法以改善零工式生產排程系統問題的求解效能及效率。首先,針對零工式生產排程系統的作業程序與限制、績效衡量標準和派工法則做一探討。其次,介紹遺傳演算法的基本架構和重要因子,並整理出遺傳演算法的各種編碼方式和交配方式;然後介紹改良式遺傳演算法與傳統遺傳演算法的不同,說明改良式遺傳演算法的理論概念,藉由多個改善策略,來改善傳統遺傳演算法的搜索效率。此外,針對本研究之系統模型架構,對測試問題做一系列的測試,共分兩部分。第一部分探討傳統遺傳演算法的各個控制參數,如交配率、突變率和族群數的變化對於三種不同交配方式的傳統遺傳演算法之影響;第二部分則探討改良式遺傳演算法對於零工式生產排程系統的改善狀況,並與傳統遺傳演算法做比較。 關鍵字:零工式生產排程系統、遺傳演算法、改良式遺傳演算法、交配率、突變率、族群數。
ABSTRACT In recent years, the heuristic algorithm has gradually been adopted to deal with NP-hard problems in the combinatorial optimization. Among these combinatorial optimal problems, the job-shop scheduling problems are usually encountered in the production managements. In this thesis, the modified genetic algorithms (MGA) are proposed to analyze and further apply to the job-shop scheduling problems for improving the calculation effectiveness and efficiency. First of all, discussions on the operation procedures, restrictions, dispatching and effectiveness evaluating criteria of the job scheduling problems are made. Then, the fundamental framework and some important factors are introduced, and the coding and the cross-over formats of the GA are identified. After introducing several improvement strategies into the searching solvers, an MGA is formulated and used to compare the searching efficiency with the conventional GA. A series of testing examples on the controlling factors, such as cross-over rate, mutation rate and population size, are also conducted to analyze the influences of three different crossover formats on the conventional GA. Moreover, the solving and searching efficiencies of the MGA applied to the job-shop scheduling system are also discussed and compared with the traditional GA. Keywords: Job-shop scheduling problems, genetic algorithm, modified genetic algorithm, crossover rate, mutation rate, population size.
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M91510006
http://ntour.ntou.edu.tw/ir/handle/987654321/14056
Appears in Collections:[系統工程暨造船學系] 博碩士論文

Files in This Item:

File Description SizeFormat
index.html0KbHTML267View/Open


All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback