臺灣博碩士論文加值系統

近年，工業4.0的革新已相繼在企業間展開。預測性維護是智慧型生產系統中重要的一環，透過感測器的監測及資料分析使生產系統得以在失控前先一步進行維修而提高系統可靠度。傳統上的經濟生產量模型假設生產系統的產出皆為良品，但實際上生產過程中卻有可能產生不良。生產系統可能由可控狀態轉移到失控狀態而導致不良率的增加。因此，將收入來源區分為良品與瑕疵品較於貼近實務。在現實狀況中，供應商經常提供零售商信用交易，以減輕零售商財務負擔及增加自身銷售。本研究的第一個模型建立預測性維修下建立非完美生產系統，第二個模型則延伸考慮當供應商提供信用交易之情況。模型的目標為決定最佳的生產期間及預測性維護投入程度已最大化總期望利潤。最後，我們探討系統參數對決策變數與利潤的影響，並提出相關的結論與管理意涵。

Lately, Industry 4.0 revolution is initiated among enterprises. As a part of intelligence production system, predictive maintenance is able to estimate production system breakdown points through cyber-physics sensors and data analysis, which is capable executing maintenances before system breakdown and improving system reliability. The traditional EPQ model assumes production system all products are perfect quality, which is unrealistic. In reality, production system may produces imperfect items, and shifting from “in-control” to “out-of-control” statuses, causing a higher defective rate. Due to system characteristic considered revenue sources as two types of item depends on quality is more reasonable. In reality, suppliers often provides trade credit to customers, in order to release their financial pressure and increases supplier’s demands. In this study, first imperfect economic production quantity model we proposed is under predictive maintenance. The second model is taking trade credit financing arrangement as an additional extension. Primary objective of models are determine optimal production runtime, predictive maintenance efforts level and maximized expected total revenue. In the finish, we characterized the relation among decisions and profit so as to obtain conclusions and relevant managerial phenomena.

摘要 I
ABSTRACT II
CONTENTS IV
LIST OF FIGURES V
LIST OF TABLES V
CHAPTER 1 5
1.1 Background and Motivation 2
1.2 Research Objectives 4
1.3 Research Organization 4
CHAPTER 2 6
2.1 EPQ model and Imperfect Production Process 6
2.2 Maintenance Policy 7
2.3 Trade Credit 9
CHAPTER 3 11
3.1 Notations and Assumptions 11
3.2 Mathematical Model 13
3.3 Solution Approach 17
3.4 Numerical Examples 17
3.5 Sensitivity Analysis 21
CHAPTER 4 21
4.1 Mathematical Model 24
4.2 Solution Approach 28
4.2.1 Optimal production runtime 28
4.2.2 Optimal Solution and Algorithm Development 34
4.3 Numerical Examples 38
4.4 Sensitivity Analysis 41
CHAPTER 5 46
5.1 Conclusions 47
5.2 Future Research 49
REFERENCE 50

LIST OF FIGURES
Figure 1. Total On-hand inventory of model 1 16
Figure 2. Production Period Length influences Expected Total Revenue 19
Figure 3. Predictive Maintenance Efforts Level influences Expected Total Revenue 19
Figure 4. 3D graphic of Predictive Maintenance Efforts Level, Production Period Length influences and Expected Total Revenue 20
Figure 5. Total On-hand inventory of model 2 24
Figure 6. Production Period Length influences Expected Total Revenue 39
Figure 7. Predictive Maintenance Efforts Level influences Expected Total Revenue 40

LIST OF TABLES
Table 1. Numerical Example for Model1 18
Table 2. Sensitivity analysis of Set-up Cost for model 1 22
Table 3.Sensitivity analysis of Holding Cost for model 1 22
Table 4.Sensitivity analysis of Corrective Maintenance Cost for model 1 22
Table 5.Sensitivity analysis of Predictive Maintenance Cost for model 1 22
Table 6.Sensitivity analysis of Perfect Item Selling Price for model 1 23
Table 7.Sensitivity analysis of Imperfect Item Selling Price for model 1 23
Table 8. Numerical Example for Model2 39
Table 9. Sensitivity analysis of Set-up Cost for model 2 42
Table 10. Sensitivity analysis of Holding Cost for model 2 42
Table 11. Sensitivity analysis of Corrective Maintenance Cost for model 2 43
Table 12. Sensitivity analysis of Predictive Maintenance Cost for model 2 43
Table 13. Sensitivity analysis of Perfect Item Selling Price for model 2 44
Table 14. Sensitivity analysis of Imperfect Item Selling Price for model 2 44
Table 15. Sensitivity analysis of Credit Period for model 2 45
Table 16. Sensitivity analysis of Interest Paid for model 2 45
Table 17. Sensitivity analysis of Interest Earned for model 2 46

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