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研究生:李孟瑾
研究生(外文):Meng-Ching Lisa Lee
論文名稱:運用分解技術於多廠生產規劃之研究
論文名稱(外文):Using Decompostion Technique to Solve Multiplant Produciotn Planning Problems
指導教授:蔣明晃蔣明晃引用關係郭瑞祥郭瑞祥引用關係
指導教授(外文):David M. ChiangRuey-Shan Guo
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:商學研究所
學門:商業及管理學門
學類:一般商業學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:64
中文關鍵詞:分解技術多廠生產規劃
外文關鍵詞:Decompostion TechniqueMultiplant Production Planning
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隨著獨善其身的局部最佳化方法不再提供企業有效的競爭力,業界對供應鏈管理的興趣日漸濃厚。然而,許多企業仍舊因循傳統,繼續以一般的經驗法則管理生產規劃與排程的問題。此外,即使企業有心運用整體規劃技術建立供應鏈管理的模型,往往也因計算工作的龐雜而望之卻步。因此,本論文主旨在於提出一個新的規劃模型,以期透過更具效率的方式解決生產規劃與排程的問題。
本論文中運用分解法對一個生產規劃與排程模型進行運算,將原有的大型混合性整數規劃模型分解為一個主模型與數個子模型。該分解模型可根據產能、物料、以及需求的限制,進行總成本的最小化,並產生明顯可行而良好的生產排程。分解法會反覆進行運算,直到與最佳值之差異小於0.05時方才停止,並產生出最終的解。
本論文以模擬方式,比較經驗法則、整體規劃模型、以及分解規劃模型三種規劃方式,在四十八種不同情境下的績效,並加以比較。研究結果中,分解規劃模型可以提供近乎最佳的績效,一般的經驗法則反而無法維持規劃績效的穩定。儘管與經驗法則和整體規劃模型相比,分解規劃模型在簡單的情境中運算耗時較久﹔隨著情境越趨複雜,後者的運算績效卻會逐漸超越前兩者。因此,在實務運用上,分解規劃模型顯然是較理想的生產規劃排程工具。
As enterprises are no longer effectively compete in isolation of their suppliers and other entities in the supply chain, interest in the concepts of supply chain management has steadily increased. However, there are still many enterprises continue their traditional practices, using common business rules to manage their production planning and scheduling problems. Furthermore, modeling SCM concepts using the monolithic approach is a computational challenging task. Therefore, this thesis aims to implement a model that can solve the production planning and scheduling problems in a more effective way.
In this thesis, a production planning and scheduling model is implemented using decomposition technique, which breaks down a large-scale mixed-integer production-planning problem into a master model and several smaller submodels. The model minimizes total production costs and generates a demonstrably good production schedules subject to materials, capacity and demand constraints. The final solution of the decomposition model is found by an iterative procedure, which stops when the optimality gap is smaller than 0.05.
The three approaches, business rules, monolithic model and decomposition model are simulated under 48 scenarios and the results are compared. The decomposition model can generate a solution very close to the optimal whereas the business rules can not guarantee a stable performance. Even though the simulation time required in solving a simple production problem is not short enough to compete with the business rules or the monolithic model, however, as the production problems gets complicated, the decomposition model will outperform the business rules and the monolithic model. For this reason, the decomposition model would be a better production planning and scheduling tool in practice.
Contents
Acknowledgements i
論文摘要 ii
Abstract iii
Contents iv
Figures vi
Tables vii
Chapter 1 Introduction 1
1.1 Motivations of This Study 1
1.2 The Objectives of the Thesis 3
1.3 Research Methodology 4
1.4 Structure of the Thesis 4
Chapter 2 Literatures Reviews 6
2.1 Supply Chain Management 6
2.1.1 Definitions 6
2.1.2 Performance Measures of SCM 7
2.2 Advance Planning and Scheduling (APS) 9
2.2.1 i2 Solution Provider 10
2.3 Production Planning and Scheduling Model in SCM 11
2.3.1 Supply Chain Modeling 11
2.3.2 Multiplant Production Planning and Scheduling in SCM 12
2.3.3 Decomposition Method 13
Chapter 3 15
The Decomposition Approach to Multiplant Production Planning and Scheduling Problems 15
3.1 Multiplant Production Planning and Scheduling Formulations 15
3.1.1 Model Assumptions 15
3.1.2 The Monolithic Model 16
3.2 An Overview of the Decomposition Approach 21
3.2.1 Heuristics 24
3.3 The Decomposition Model 25
3.3.1 Master Model 26
3.3.2 Submodel (plant-based, j) 27
Chapter 4 Simulations and Comparisons 33
4.1 Design of Scenarios 34
4.1.1 Choice of Parameters 34
4.1.2 Scenarios 35
4.2 Business Rules 37
4.3 Case One: Small Scale, Loose Capacity 38
4.3.1 Production Cost and Runtime 38
4.4 Case Two: Small Scale, Tight Capacity 41
4.4.1 Production Cost and Runtime 42
4.5 Case Three: Large Scale, Loose Capacity 44
4.5.1 Production Cost and Runtime 45
4.6 Case Four: Large Scale, Tight Capacity 47
4.6.1 Production Cost and Runtime 47
4.7 Comparison between the Business Rules, the Monolithic Model and the Decomposition Model 49
Chapter 5 Conclusions 51
5.1 Conclusions 51
4.8 Contributions of the Thesis 53
4.9 Recommendations for Future Developments 53
References 55
Appendix A: Parameters for the Simulations 57
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