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研究生:詹清翔
研究生(外文):Chan, Ching-Hsiang
論文名稱:自動搬運車中央控制系統之多目標規劃
論文名稱(外文):Multi-objective Optimization of AGV Centralized Control System
指導教授:王小璠王小璠引用關係
學位類別:碩士
校院名稱:國立清華大學
系所名稱:工業工程與工程管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:73
中文關鍵詞:自動搬運車路徑規劃取貨與送貨最小化能源多產品多目標規劃智慧控制演算法
外文關鍵詞:Automated Guided Vehicle (AGV) routing;Pickup and DeliveryEnergy MinimizationMultiple ProductsMultiple ObjectivesIntelligent Control Algorithm
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本研究目的為解決以自動搬運車同時取送多貨物之最佳派車及路徑規劃問題,以期降低車輛派遣成本與能源之消耗。為解決此問題,我們參考相關文獻,發展出以自動搬運車中央控制系統爲主軸的數學規劃模型,解決自動搬運車路線規劃問題(AGVRP)。根據我們所提供的搬運成本與能源消耗間的權衡機制,決策者可經由規劃求解軟體(CPLEX 12.5)得到最佳滿意解。
然而,因為此數學規劃模型屬於靜態模型,當系統在運作時,如突然有未在規劃內的任務產生時,靜態系統無法即時處理該任務。因此,爲解決動態環境之即時任務指派與路徑規劃問題,我們利用模擬軟體(FLEXSIM) 模擬動態環境以建構模擬模型,並且提出稱爲『智慧控制演算方法』的架構。此方法結合模擬模型進行運算後,可以同時解決任務指派與路徑規劃的問題。在此架構下的內容,包含智慧控制演算方法與兩個基於此演算方法之派遣法則,分別是最短路派遣法則與最佳車輛使用率派遣法則。為確保模擬解的精確度,我們比較確定性模型(AGVRP)和智慧控制演算方法。結果顯示本架構在小規模生產下的結果接近最佳解。而在大規模的問題中,與常用之最近車輛法則[1, 2]比較,我們所提出的方法較為優異。
最後,我們所提之智慧控制系統以合作公司為案例,已驗證其可行性與效益。

Due to the rise of labor cost and the requirement of quality stability, industrial automation has become a solution for enterprises to strengthen the competitiveness and reduce the operational costs in recent years. In the field of industrial automation, automated guided vehicle (AGV) system has been generally adopted for transporting semi-manufactured goods, components or products between workstations to facilitate production processes in an unmanned factory. In order to satisfy production processes, the planned routes of AGVs which coordinate workstations and a warehouse play a critical rule to enhance system performance.
This study first investigates the important factors which affect the vehicle routing performances of the AGVs and has the following observations: (1) In-time material delivery along workstations smoothens the production process; (2) The lighter “en route vehicle weight” alleviates energy wastes; (3) The increased space utilization of an AGV reduces the fleet size of AGVs. Based on the aforementioned analysis, a mathematical model in the form of multi-objective integer programming model is developed to obtain the optimal vehicle routings for pick-ups and deliveries of multiple products/components along the production flows such that energy consumption and operational cost can be minimized. Moreover, in order to have further applications in a practical circumstance, the simultaneously dispatching and routing algorithm, called Intelligent Control Algorithm, is proposed to enhance system performance, such as the moving distance and the completion time of the products.
Finally, an illustrative example from an assembly company of Taiwan is presented to verify our proposed deterministic model and dynamic algorithm embedded in the simulation software, Flexsim. With given layout and production flows, the result shows that the pick-up and delivery routes of AGVs can be determined optimally and ensured feasibility with sufficient fleet in deterministic model and our proposed rules based on the algorithm can outperform the conventional nearest-vehicle dispatching rules.

TABLE OF CONTENT II
ABSTRACT IV
中文摘要 V
ACKNOWLEDGMENT VI
FIGURE AND TABLE CAPTIONS IX
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Motivation 2
1.3 Organization of Thesis 3
CHAPTER 2 LITERATURE REVIEW 4
2.1 Deterministic Models 4
2.1.1 AGV Systems in relation to Traveling Salesman Problems 4
2.1.2 Vehicle Routing Problem 6
2.2 Dynamic Models using Simulation 8
2.2.1 Dispatching Rules in AGV System Simulation 8
2.2.2 Routing Strategies in AGV System Simulation 9
2.3 Simulation Optimization 10
2.4 Concluding Remarks 11
CHAPTER 3 MODEL FORMULATION 12
3.1 Problem Statement 12
3.2 Assumption 14
3.3 Preprocess Procedure 20
3.4 The Proposed Deterministic AGVRP Model 15
3.4.1 Notations 15
3.4.2 The Multi-objective Integer Non-linear Programming 16
3.4.3 Properties of the Deterministic AGVRPTW Model 20
3.5 A Case Study in Deterministic Environment 20
3.5.1 Description of the Problem with Input Data 21
3.5.2 Numerical Results and Analysis 25
3.6 Concluding Remarks 36
CHAPTER 4 INTELLIGENT CONTROL ALGORITHM IN DYNAMIC ENVIRONMENT 37
4.1 Problem Description with the Proposed Simulation Model 37
4.1.1 Description of the Problem 37
4.1.2 The Simulation Model 39
4.2 Framework of the Proposed Intelligent Control Algorithm 42
4.2.1 The Real-Time Routing Cost 42
4.2.2 Control Rules and Procedure 44
4.2.3 Summary and Discussion 52
4.3 A Real Case Example in Dynamic Environment 52
4.3.1 Description of the Input Data 53
4.3.2 The Simulation Results 54
4.3.3 Comparative Analysis with the Deterministic Model 58
4.3.4 Comparative Analysis and Discussion 61
4.4 Concluding Remarks 64
CHAPTER 5 SUMMARY AND CONCLUSION WITH FUTURE WORK 66
5.1 Summary and Conclusion 66
5.2 Future Work 67
APPENDIX Ⅰ 72
APPENDIX Ⅱ 73

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