漫長製程生產的特徵為生產週期時間大於規劃的時間單位，因此，傳統產品組合規劃方法在工作量計算方面有諸多不合理之處。半導體生產流程之週期時間具有漫長製程之特性，本篇論文針對漫長製程之生產型態，提出產品組合規劃方法，其中並考慮機台間互相支援、平穩產出量之情形。本研究主要包含以下幾個部分: 規劃時間單位之分析、多期多種型態產品之混合整數線性規劃及長期產品組合軌跡問題。
本研究主要貢獻為分析不同規劃時間單位之均勻性對工作量計算方法的相適度，並建議產品組合規劃應以週為規劃之時間單位。由於長期產品組合軌跡模型之限制式過於繁多，造成求解之不易，本文並提出一套系統化、有效率的求解過程。本研究針對產品組合規劃問題，實作一軟體系統，提供生產決策之分析，進而提昇產品組合規劃之敏捷性。

Long process flow production is characterized by the cycle time of production being longer than the planning time bucket. Semiconductor manufacturing process has the feature of long cycle time. Conventional product mix planning method is insufficient in workload calculation. In this thesis, a product mix planning methodology is proposed for long process flow production. Tool backup and smoothing production volume are also considered in our model. The content of this research includes: analysis of time granularity level, a mixed integer linear programming for multiple, heterogeneous classes of products, and a scaled down and efficient approach to product mix trajectory for long planning horizon.
The major achievement of this research is that fidelity ratio of time granularity is analyzed and the week is found to be a robust time bucket size for product mix planning. Moreover, an efficient solution procedure has been developed for product mix trajectory for long planning horizon. A software system is implemented to provide both product mix solutions and sensitivity data for product price and tool capacity. Used as a planning tool, the manufacturing system will improve production agility under dynamic market conditions.

Chapter 1Introduction1
1.1Motivation1
1.2Background1
1.2.1Categories of product type1
1.2.2Process flow characteristics2
1.3Problem description3
1.3.1Properties in nature4
1.3.2Constraints description6
1.4Thesis organization6
Chapter 2Literature Review7
2.1Theory of constraints (TOC) model7
2.1.1Introduction to theory of constraints (TOC)7
2.1.2Application of TOC in product mix planning8
2.1.3The revised TOC product mix heuristic11
2.2Traditional cost accounting14
2.3Linear programming model and resource allocation16
Chapter 3Product Mix Planning17
3.1Workload calculation17
3.1.1Generation of unit workload17
3.1.2Generation of aggregate and total workload20
3.2Precision workload calculation21
3.2.1Experiment results of equal-length month and calendar month cases24
3.3Product mix optimization30
3.3.1An optimization model32
3.3.2Model with tool backup35
3.3.3Efficiency comparison of models with or without tool backup37
3.4Product mix trajectory planning with bottleneck analysis38
3.5Sensitivity analysis44
Chapter 4Software Module47
4.1System architecture47
4.2Optimization modules51
4.2.1Mix optimization51
4.2.2Mix optimization with bottleneck tools54
4.3Output management55
4.3.1Product mix output55
4.3.2Workload distribution and tool utilization56
4.3.3Bottleneck analysis58
4.4Demand management59
4.4.1Creation of new demands59
4.4.2Demand updating and deleting60
Chapter 5A Numerical Study62
5.1System environment62
5.2Significance of lead time offset62
5.3Comparisons between different objectives of models63
5.4Smoothing product mix module64
5.5Comparison of tool backup66
5.6A numerical study of product mix trajectory70
Chapter 6Conclusions and Suggestion74
6.1Conclusions74
6.2Future work74
Reference:76
Appendix A:Attributes of Database Schema79
Appendix B:Bottleneck tool analysis82
Appendix C:Product mix outputs with or without lead time offset84
Appendix D:Part Quantity for Different Objectives87
Appendix E:Smoothing Product Mix Planning89
Appendix F:Comparison of Tool Backup91
Appendix G:Product Mix Trajectory Planning94

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