研究生: |
張祐騰 Chang, Yu-Teng |
---|---|
論文名稱: |
以MOEA/D結合適應性區域搜尋求解多目標定序流線型工廠排程問題 MOEA/D with adaptive local search for multiobjective permutation flowshop scheduling problems |
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: | 60 |
中文關鍵詞: | 多目標 、定序流線型工廠排程問題 、區域搜尋 |
英文關鍵詞: | MOEA/D |
DOI URL: | https://doi.org/10.6345/NTNU202203489 |
論文種類: | 學術論文 |
相關次數: | 點閱:126 下載:20 |
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本論文將MOEA/D應用於求解多目標定序流線型工廠排程問題(multi-objective permutation flowshop scheduling problem),已知多目標定序流線型工廠排程問題是一個 NP-hard 問題,無法確保在多項式時間內將該問題求得最佳解。在這個問題中有多個零件(job)需要依序送入機器(machine)中加工,而每個零件根據製程(operation)不同而有不同的加工時間(processing time);所有零件皆加工完成的時間為最大完工時間(makespan),而每個零件的完工時間總和為總流程時間(total flow time),我們希望能同時最小化最大完工時間與總流程時間,但縮短最大完工時間可能使得總流程時間增加,反之亦然;然而,我們可以求出非凌越解(non-dominated solution),這些解在目標空間形成一條柏拉圖前緣(Pareto front),我們的目標是求解得到盡量靠近真實解,且分佈越完整的柏拉圖前緣。
過往文獻中,使用 MOEA/D 這種將目標空間(objective space)分解的方法並不多;本論文深入探討 MOEA/D 流程中各個操作對效能之影響;除此之外,我們使用區域搜尋強化解的品質,並探討不同搜尋方式對效能之影響。我們使用Taillard 測試問題集進行實驗分析,並與知名演算法比較,本論文提出的演算法在中、大型的問題具有較好的效果。
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