Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm

碩士 === 國立臺灣科技大學 === 工業管理系 === 97 === Job-shop scheduling problem (JSP), which was wildly used in industries, plays a vital role in manufacture scheduling. Many of the high-tech industries such as semiconductor industries, TFT-LCD industries belong to the Job-shop scheduling. Nevertheless, due to t...

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Main Authors: Chi-Hsun Chung, 鍾奇勳
Other Authors: Ruey-Huei Yeh
Format: Others
Language:zh-TW
Published: 2009
Online Access:http://ndltd.ncl.edu.tw/handle/63294904306332949902
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spelling ndltd-TW-097NTUS50410512016-05-02T04:11:38Z http://ndltd.ncl.edu.tw/handle/63294904306332949902 Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm 以波茲曼基因演算法求解零工式生產排程問題 Chi-Hsun Chung 鍾奇勳 碩士 國立臺灣科技大學 工業管理系 97 Job-shop scheduling problem (JSP), which was wildly used in industries, plays a vital role in manufacture scheduling. Many of the high-tech industries such as semiconductor industries, TFT-LCD industries belong to the Job-shop scheduling. Nevertheless, due to the variation of JSP, its combinatorial optimization problem in scheduling is recognized as one of the most complicated NP-hard problems. Many experts and scholars use the Generic algorithm to seek out the JSP problem, and its powerful searching ability of Genetic algorithm (GA) was widely applied in scheduling problem. However, the insufficiency of searching partial area in GA makes the process of evolutionary searching easily fall into the local optimal solution, lowering the efficiency of seeking out the optimal solution. Based on this phenomenon, this study combines GA with Boltzmann function in Simulated Annealing algorithm, which is characterized as not easily fall into local optimal solution, developing Boltzmann Genetic Algorithm (BGA), and aims to compare the quality and efficiency between BGA and traditional GA in minimum makespan in JSP. The result of this study indicates the advantageous of BGA over traditional GA in seeking out JSP, suggesting that the BGA can save extra time and cost, and benefit industries in planning manufacturing scheduling. Ruey-Huei Yeh 葉瑞徽 2009 學位論文 ; thesis 58 zh-TW
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description 碩士 === 國立臺灣科技大學 === 工業管理系 === 97 === Job-shop scheduling problem (JSP), which was wildly used in industries, plays a vital role in manufacture scheduling. Many of the high-tech industries such as semiconductor industries, TFT-LCD industries belong to the Job-shop scheduling. Nevertheless, due to the variation of JSP, its combinatorial optimization problem in scheduling is recognized as one of the most complicated NP-hard problems. Many experts and scholars use the Generic algorithm to seek out the JSP problem, and its powerful searching ability of Genetic algorithm (GA) was widely applied in scheduling problem. However, the insufficiency of searching partial area in GA makes the process of evolutionary searching easily fall into the local optimal solution, lowering the efficiency of seeking out the optimal solution. Based on this phenomenon, this study combines GA with Boltzmann function in Simulated Annealing algorithm, which is characterized as not easily fall into local optimal solution, developing Boltzmann Genetic Algorithm (BGA), and aims to compare the quality and efficiency between BGA and traditional GA in minimum makespan in JSP. The result of this study indicates the advantageous of BGA over traditional GA in seeking out JSP, suggesting that the BGA can save extra time and cost, and benefit industries in planning manufacturing scheduling.
author2 Ruey-Huei Yeh
author_facet Ruey-Huei Yeh
Chi-Hsun Chung
鍾奇勳
author Chi-Hsun Chung
鍾奇勳
spellingShingle Chi-Hsun Chung
鍾奇勳
Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm
author_sort Chi-Hsun Chung
title Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm
title_short Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm
title_full Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm
title_fullStr Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm
title_full_unstemmed Solving Job-Shop Scheduling Problems by Boltzmann Genetic Algorithm
title_sort solving job-shop scheduling problems by boltzmann genetic algorithm
publishDate 2009
url http://ndltd.ncl.edu.tw/handle/63294904306332949902
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