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研究生:蔡勝旺
研究生(外文):Tsai Sheng-Wang
論文名稱:同時考量維修週期與維修活動之串並聯系統非完美預防維修規劃
論文名稱(外文):Optimizing Preventive Maintenance Strategy of Series-Parallel System Under Imperfect Maintenance
指導教授:王春和王春和引用關係
指導教授(外文):Wang Chung-Ho
口試委員:王妙伶林義貴王春和張仁煦陳幼良張子筠
口試日期:2013-05-15
學位類別:博士
校院名稱:國防大學理工學院
系所名稱:國防科學研究所
學門:軍警國防安全學門
學類:軍事學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:163
中文關鍵詞:非完美維修串並聯系統巨集啟發式演算法多目標最佳化
外文關鍵詞:Imperfect preventive maintenanceMulti-component systemMeta-heuristic algorithmsMulti-objective optimization
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本論文目的乃針對串並聯系統,建構一個使維修總成本最小化及系統平均可靠度最大化的多目標週期性非完美預防維修模式,並建構適當的限制條件,滿足實務上對系統性能的基本需求,其中維修策略的制定將同時決定系統中個別元件最適的維修週期與每一個維修時間點上最適的維修活動,所考量的維修活動包括:不實施任何預防維修活動、保養、維修及置換等四種,另外當元件發生失效時,則實施最小修復(Minimal Repair)的矯正性維修,使元件功能回復至失效前的狀態,從而建構一個多目標非完美預防維修最佳化模式,來決定最適合的維修策略。本論文以粒子群演算法與基因演算法,分別建立一個適合同時求解維修週期與維修活動的預防維修模式之改良型演算法,其中,為了解決演算法落入不可行解區域搜尋的問題,建構一種調整機制,可以提高演算法的搜索效能,此外,本論文採用以柏拉圖為基的技術(Pareto-Based Technique),將多目標非凌駕解(Non-Dominated Solutions)的觀念,分別導入粒子群演算法(Particle Swarm Optimization Algorithm)與基因演算法(Genetic Algorithm)的搜尋機制中,建立改良型粒子群演算法(Improved Particle Swarm Optimization Algorithm, IPSO)與改良型基因演算法(Improved Genetic Algorithm, IGA),以有效地求解所建構的多目標預防維修模式,從而獲得具差異性的可行維修方案。本論文導入反應曲面法(Response Surface Methodology, RSM),經由有系統的實驗規劃與實驗數據解析,適配建構維修總成本及系統平均可靠度與IPSO及IGA之搜尋參數間的反應曲面模式,從而決定搜尋參數的最佳設定值,並引用三個模擬案例及文獻的例子驗證所提出方法的有效性,後續應用於求解國軍實際案例,決策者可依實際資源的限制或對系統性能的要求,從中選取最適合的方案,拓展本論文的實務應用性。
This study aims at developing a bi-objective imperfect preventive maintenance (BOIPM) model in which the total maintenance cost and the mean system reliability are determined by optimizing the maintenance periods and maintenance activities simultaneously. The improvement factor method is used to evaluate the extent to which maintaining components can restore the component reliability. The maintenance activities including no action, mechanical service, repair, and replacement are considered in the established BOIPM model. When the components fail, corrective maintenance with minimal repair is implemented. Furthermore, two multi-objective meta-heuristic algorithms including improved particle swarm optimization (IPSO) algorithm and improved genetic algorithm (IGA) are proposed to enable simultaneous optimization of the maintenance periods and maintenance activities. An adjustment mechanism addressing the issue of particles or chromosomes falling into the infeasible area is constructed to enhance the exploring ability of the IPSO or IGA, owing to its random search mechanism. The non-dominated solutions from the idea behind Pareto optimization is designed to determine the superior particles and chromosomes and thereby obtain diversified multi-objective solutions. Maintenance engineers can simply determine the most appropriate alternative in accordance with practical system performance requirements and resource constraints. The search parameters regarding IPSO and IGA are determined via response surface methodology (RSM). Three simulated cases and an example from past study are utilized to verify the efficacy of the proposed approach. Finally, a real case regarding nine devices that constitute important components of a helicopter are used to verify the practicability of the proposed approach.
誌謝 ii
摘要 iii
ABSTRACT v
目錄 vi
表目錄 ix
圖目錄 xi
符號說明 xiii
縮寫 xvi
1. 前言 1
2. 文獻回顧 5
2.1 維修相關文獻 5
2.1.1 維修策略 5
2.1.2 非完美維修 6
2.2 多目標最佳化 9
2.2.1 接近度指標 11
2.2.2 離散度指標 12
2.2.3 展開度指標 14
2.3 粒子群演算法簡介 15
2.3.1 粒子群演算法介紹 15
2.3.2 粒子群演算法求解程序 15
2.3.3 粒子群演算法改善機制 17
2.4 基因演算法簡介 18
2.4.1 基因演算法介紹 18
2.4.2 基因演算法求解程序 19
2.4.3 基因演算法求解連續型變數的染色體編碼 21
2.5 反應曲面法 22
2.6 文獻總結 23
3. 研究方法 25
3.1模式的基本假設 25
3.2 模式建構 25
3.3 建立改良型粒子群最佳化演算法 28
3.4 建立改良型基因演算法 37
4. 成效驗證 44
4.1求解所建構的預防維修模式及所建立的演算法之成效驗證 44
4.1.1 以改良型粒子群演算法求解預防維修模式之成效驗證 47
4.1.2 以改良型基因演算法求解預防維修模式之成效驗證 73
4.2以文獻案例進行有效性驗證 93
4.2.1以改良型粒子群演算法應用於文獻案例之有效性驗證 95
4.2.2以改良型基因演算法應用於文獻案例之有效性驗證 105
5. 案例研究 110
5.1 問題描述 110
5.2 模式建構 112
5.3 以啟發式演算法求解直升機案例 115
5.3.1 以改良型粒子群演算法求解直升機案例 115
5.3.2 以改良型基因演算法求解直升機案例 135
6. 結論與未來研究方向 151
6.1 結論 151
6.2 未來研究方向 152
參考文獻 153
論文發表 161
自傳 163

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