Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems

Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems

碩士 === 元智大學 === 資訊管理學系 === 99 === In recent years, industrial manufacturing usually faces the tradeoff of multi-objective decision problems. Many researchers have become more aware of the efficiency of heuristics for solving multi-objective problems. In this paper, we improve the previous SPGA appro...

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Main Authors: Lin Hsu, 徐麟
Other Authors: 張百棧
Format: Others
Language:zh-TW
Published: 2011
Online Access:http://ndltd.ncl.edu.tw/handle/30519543702160904149
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spelling ndltd-TW-099YZU053960802016-04-13T04:17:17Z http://ndltd.ncl.edu.tw/handle/30519543702160904149 Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems 以柴比雪夫分群法建構子群體權重向量求解多目標問題 Lin Hsu 徐麟 碩士 元智大學 資訊管理學系 99 In recent years, industrial manufacturing usually faces the tradeoff of multi-objective decision problems. Many researchers have become more aware of the efficiency of heuristics for solving multi-objective problems. In this paper, we improve the previous SPGA approach and present a Sub-population Genetic Algorithm II (SPGA-II). SPGA-II takes advantage of the Tchebycheff Decomposition and effective Pareto Fronts and Reference Points generated during the evolutionary process to enhance the performance of the proposed approach. Our experimental results show that SPGA-II is able to improve the performance of SPGA in solving Parallel Machine Scheduling Problems. 張百棧 2011 學位論文 ; thesis 65 zh-TW
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language zh-TW
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description 碩士 === 元智大學 === 資訊管理學系 === 99 === In recent years, industrial manufacturing usually faces the tradeoff of multi-objective decision problems. Many researchers have become more aware of the efficiency of heuristics for solving multi-objective problems. In this paper, we improve the previous SPGA approach and present a Sub-population Genetic Algorithm II (SPGA-II). SPGA-II takes advantage of the Tchebycheff Decomposition and effective Pareto Fronts and Reference Points generated during the evolutionary process to enhance the performance of the proposed approach. Our experimental results show that SPGA-II is able to improve the performance of SPGA in solving Parallel Machine Scheduling Problems.
author2 張百棧
author_facet 張百棧
Lin Hsu
徐麟
author Lin Hsu
徐麟
spellingShingle Lin Hsu
徐麟
Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
author_sort Lin Hsu
title Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
title_short Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
title_full Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
title_fullStr Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
title_full_unstemmed Sub-population Genetic Algorithm II for Multi-objective Parallel Machine Scheduling Problems
title_sort sub-population genetic algorithm ii for multi-objective parallel machine scheduling problems
publishDate 2011
url http://ndltd.ncl.edu.tw/handle/30519543702160904149
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