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研究生： 劉顥程
Hao-Cheng Liu
論文名稱： 製造系統之無人搬運車的控制問題研究
A Study on AGV Control Problems in Manufacturing Systems
指導教授： 何應欽
Ying-Chin Ho
口試委員:
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理研究所
Graduate Institute of Industrial Management
畢業學年度： 100
語文別： 英文
論文頁數： 95
中文關鍵詞： hot lot 、模糊理論 、多屬性方法 、負載選擇 、無人搬運車 、派車法則
外文關鍵詞： Fuzzy, multiple-attribute, hot lot, vehicle dispatching, AGVs, load selection
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    • 本研究旨在發展無人化自動搬運車(AGVs)的派車方法。物料搬運在很多環境是非常重要的，例如，港口、倉儲、TFT-LCD與半導體無塵室。在自動化物料搬運設備中，無人化自動搬運車是最常見的，因為無人化自動搬運車可以很容易的適應環境，也很容易隨環境或是設備改變，變更行走路線。於是，已經有很多工廠使用無人化自動搬運車負責物料搬運。派車法則對無人化自動搬運車的能力有很重大的影響。所以本研究專注於發展無人化自動搬運車的派車方法。本研究發展了三個派車方法分別針對三個不同的環境。第一，本研究發展一個Fuzzy Logic Control (FLC)於一個job shop的環境中派遣無人化自動搬運車。第二，本研究利用分群的概念提出一個派車方法，該方法可以同時解決由Ho and Chien (2006)定義的四個派遣多載量無人化自動搬運車子問題中的兩個子問題。第三，本研究針對TFT-LCD工廠中的hot lot問題，發展出Fuzzy-Based Dynamic Bidding (FBDB)派車方法，期該法可以降低hot lot對系統帶來的負面影響。本研究利用電腦模擬測試本研究所提出的方法，而實驗結果也證實了三個方法的優越性。

    This study focuses on developing dispatching method for Automatic Guided Vehicles (AGVs) system in a manufacturing system. Material handling is critical in many facilities and manufacturing systems such as seaports, warehouse, TFT-LCD fabs, and semiconductor fabs. Among automatic material handling systems, AGVs is the most common because AGVs adapt easily to layout routes. Therefore, a lot of companies have utilized AGVs for material handling. Dispatching rule is essential to AGV’s transportation capability. Therefore, this study focuses on developing dispatching rules to dispatch AGVs. Three dispatching rules are developed for three different systems in this study. First, a Fuzzy Logic Control (FLC) is proposed for dispatching AGVs in a job shop environment. Second, a clustering-based method is proposed to solve two sub-problems of dispatching multiple-load AGVs, which are defined by Ho and Chien (2006), simultaneously. Third, a Fuzzy-Based Dynamic Bidding (FBDB) method is developed for dispatch AGVs to reduce the negative impact brought by hot lots in a TFT-LCD fab. Computer simulation is used to examine the proposed methods, and the superiority of the proposed methods is given by analyzing the results.

    摘要 I Abstract II 誌 謝 III Contents IV List of Figures VI List of Tables VII Chapter 1. Introduction 1 Chapter 2. Literature review 4 2.1 AGV Dipsatching 4 2.2 Scheduling Problem in TFT-LCD and Semiconductor Fabs 7 Chapter 3. A Multiple-Attribute Fuzzy Logic Control for Dispatching Multiple-Load AGVs 10 3.1 Introduction 10 3.2 FLC 10 3.2.1 Fuzzification 10 3.2.2 Inference Rules 12 3.2.3 Defuzzification 14 3.3 Simulation Experiments 15 3.3.1 Environment 15 3.3.3 Throughput Performance 19 3.3.4 Tardiness Performance 21 3.3.5 Comparison 23 3.4 Findings 25 Chapter4. A Multiple-Attribute Method for Concurrently Solving the Pickup-Dispatching Problem and the Load-Selection Problem of Multiple-Load AGVs 27 4.1 Introduction 27 4.2 The Proposed Method 28 4.3 Method for the Task-Determination Problem 29 4.4 The Method for the Delivery-Dispatching Problem 29 4.5 The Proposed Method 29 4.5.1 Preparation – The First Stage of the Proposed Method 30 4.5.2 Clustering – The Second Stage of the Proposed Method 33 4.5.4 Execution – The Fourth Stage of the Proposed Method 35 4.6 Simulation Experiments 36 4.7 Analysis of Simulation Results 41 4.7.1 The Proposed Method vs. Category B Methods 41 4.7.2 The Proposed Method vs. Category C Methods 43 4.7.3 Category C Methods vs. Category B Methods 44 4.7.4 The Proposed Method vs. Category D Methods 46 4.8 Findings 47 Chapter 5. Managing the Production of Hot Lots and Regular Lots in a TFT-LCD Fab 49 5.1 Introduction 49 5.2 Proposed Method 50 5.2.1 Problem Environment and the Three Problems Studied Here 50 5.2.2 The lot selection of interbay AGVs 52 5.2.3 The lot selection of intrabay machines 59 5.2.4The proposed EPT (Earliest Possible Time) method for the photo bay selection of lots 61 5.3 The methods once used by the case company 65 5.4 Simulation Experiments 68 5.4.1 The Performance of Hot Lot Ratios 70 5.4.2 The Performance of Lot Selection Methods 71 5.4.3 The Performance of Photo Bay Selection Methods 74 5.5 Findings 77 Chapter 6. Conclusion 79

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