研究生: |
王映萱 Wang, Ying-Suan |
---|---|
論文名稱: |
以分解型演化演算法求解多目標資源限制專案排程問題 A Decomposition-based Memetic Algorithm for Multiobjective Resource Constrained Project Scheduling Problem |
指導教授: |
蔣宗哲
Chiang, Tsung-Che |
學位類別: |
碩士 Master |
系所名稱: |
資訊工程學系 Department of Computer Science and Information Engineering |
論文出版年: | 2017 |
畢業學年度: | 105 |
語文別: | 中文 |
論文頁數: | 71 |
中文關鍵詞: | 資源限制專案排程問題 、多目標最佳化問題 、多目標啟發式演算法 |
DOI URL: | https://doi.org/10.6345/NTNU202203485 |
論文種類: | 學術論文 |
相關次數: | 點閱:140 下載:10 |
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目前求解資源限制專案排程問題 (Resource-Constrained Project Scheduling Problem, RCPSP) 的文獻,大多注重於求解單目標問題,又以專案完工時間 (makespan) 當作目標為大多數。而在實務中,專案經理對專案考慮的目標往往是多方面的,除了專案完工時間之外,也須考量如何在讓所有的工作在所規定的時間內完成,避免工作的延遲導致成本的大幅提高。因此本研究針對最小化專案完工時間以及最小化總延遲時間 (total tardiness) 兩項目標進行求解。
本研究提出MOMA/D-IGR來求解此問題,改良自MOEA/D [7],為了增加族群的多樣性,在環境選擇機制上,讓子代可取代的個數限制為一個,並且不讓相同個體進行取代更新。接著提出四種區域搜尋策略,希望能讓族群中的個體分採用求解方向對應之區域搜尋方法。實驗的測試問題採用Xiao等人 [15] 所提出的測試問題集,並與Xiao等人 [15] 所提出的6種演算法進行比較,分別為SPEA2、SPEA2-EM、NSGAII、NSGAII-EM、MOEA/D 以及MOEA/D-EM。實驗結果顯示MOMA/D-IGR能得出最好的效果。
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