具雙向路徑軌道之梯形機器佈置問題探討

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姓名

李佳諭(Chia-yu Lee) 查詢紙本館藏

畢業系所

工業管理研究所

論文名稱

具雙向路徑軌道之梯形機器佈置問題探討

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摘要(中)

設施規劃之績效與工廠產能之間具有高度關聯性。設施規劃即是將空間中所存在之各項設施進行佈置，以期達到有效之生產佈置方式。在許多設施規劃相關之文獻中，大部分研究皆針對線性佈置與封閉迴圈佈置進行研究，相較之下，過去在梯形佈置上的相關研究並不豐富，但梯形佈置之變化與彈性皆較線性佈置與封閉迴圈佈置為佳，且較開放式佈置形式易於規劃，因此本研究希望針對梯式佈置建立一套系統化之設計方式，以期有效利用此佈置形式之優勢，達到更有效率之設施佈置規劃。
本研究主要目的是在彈性製造系統環境下，將所需配置的機器依照梯形設施佈置之形式進行排列，並達到機器間總流量距離最小化之目標。本研究首先將此問題分成兩個Phase進行解題：在Phase I階段，本研究應用過去使用在群組技術中單元設計的四種相似係數法（similarity coefficient method）與兩種群聚演算法將機器分群；接著，利用各階層間所屬之機器流量計算階層間的流量，使用三種流線分析法（FLA；flow line analysis）決定階層擺放位置；最後，利用數學規劃模型，決定階層內各機器的正確位置。本研究針對上述步驟進行實驗，以了解使用不同方法所得到的績效表現，找出一組相似係數法、分群方法與流線分析法之最佳搭配組合，並以之當作Phase II階段之第一步驟與第二步驟之初始解，並使用tabu search分別改善Phase II階段的第一步驟與第二步驟之初始解，探討有無使用tabu search改善對於績效表現之差異。

摘要(英)

In the past, most of the research in facility layout was focus on the closed-loop layout and linear layout. There was few study focus on “ladder layout,” even it is also one of the popular layout types in FMS. Therefore, a systematical procedure of building up the ladder layout is proposed in this study.
In the first stage, the similarity coefficient method is used to find out the best result of machine grouping. In the second stage, this study uses the technique of the flow line analysis, FLA, to arrange the machine groups. Then, a mathematic model is proposed to adjust the final point location of the each machine in its ladder level. In the end of the first two stages, the tabu search , TS, is introduced to improve the initial solution of each stage. There are four similarity coefficients and two clustering algorithms in the first stage and three heuristics in the second stage. All the combinations of the methods are experimented in FMS environment to show its performance and compared with the ones without improvement.

關鍵字(中)

★ 梯形機器佈置
★ 設施規劃
★ 相似係數法
★ 分群方法
★ 流線分析法

關鍵字(英)

★ ladder layout
★ facility layout
★ similarity coefficient method
★ flow line analysis

論文目次

目錄 iii
圖目錄 v
表目錄 vi
第一章緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究環境與假設 3
1.4 研究架構與流程 4
第二章文獻探討 5
2.1 設施規劃 5
2.1.1 圖解法 5
2.1.2 數學規劃模式與啟發式演算法 6
2.1.3 人工智慧法 7
2.1.4 禁忌搜尋法 8
2.1.5 其他 9
2.2 機器佈置 10
2.3 分群方法與相似係數 13
2.3.1 分群與單元設計方法之分類 14
2.3.2 相似係數法 14
2.3.3 相似係數 16
第三章研究方法 18
3.1 方法架構與流程 18
3.2 相似係數法 22
3.2.1 相似係數矩陣之計算 24
3.2.1.1 Jaccard 相似係數（JSC） 24
3.2.1.2 Ochiai相似係數（OSC） 25
3.2.1.3 Sokal and Sneath 2相似係數（SS2SC） 25
3.2.1.4 Shafer and Rogers相似係數（Max SC） 27
3.2.2 聚群演算法之步驟 28
3.2.2.1 CA-1分群方法 28
3.2.2.2 CA-2分群方法 32
3.3 流線分析法 34
3.3.1 Method 5分析法 35
3.3.2 NEH-based 分析法 38
3.3.3 Method 1 分析法 40
3.4 禁忌搜尋法 41
3.4.1 分群改善說明 42
3.4.2 階層排列改善說明 45
3.5 數學規劃模型 48
第四章實驗設計與分析 55
4.1 實驗環境與假設 55
4.2 第一部份實驗 59
4.2.1 機器分群 59
4.2.2 階層排列 70
4.3 第二部份實驗 71
4.3.1 tabu search改善 72
4.3.2 數學模型 76
第五章結論與建議 80
5.1 結論 80
5.2 後續研究建議 81
參考文獻 82

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指導教授

何應欽(Ying-chin Ho)

審核日期

2014-7-25

推文