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研究生:李珮晶
研究生(外文):Lee Pei Chin
論文名稱:以本體論為基之多代理人系統以支援需求鏈管理-以IC設計股務為例
論文名稱(外文):Ontology-based multi-agent system for Supporting Demand Chain Management: the Case of IC Design Services Firms in Taiwan
指導教授:朱詣尹朱詣尹引用關係
指導教授(外文):Yee-Yeen Chu
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:2008
畢業學年度:96
語文別:英文
論文頁數:91
中文關鍵詞:需求鏈本體論多代理人系統
外文關鍵詞:Demand Chain Management (DCM)OntologyMulti-Agent System (MAS)
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隨著快速變化的市場需求及資訊科技的發展,企業逐漸重視需求鏈管理(Demand Chain Management)所帶來的效益並視之為提升競爭力的新議題。這使得企業可以獲得最新的市場需求資訊以便及時開發需求產品,滿足市場需求。因此,需求鏈上的成員必須溝通和合作以達到結合需求資訊、縮短前置時間及減少成本。
本研究是以台灣IC設計服務公司為背景,探討以本體論為基之多代理人系統如何支援需求鏈管理並促使跨公司團隊之間的溝通與取得及時需求資訊。多代理人系統可以讓來自不同公司的代理人在特定的領域裡以本體論理的架構與規則,直接進行有效的協調合作。需求鏈參與成員之間緊密的連結關係呈現一個相輔相佐的關係,彼此之間的需求知識交流也為了達到瞭解與回應需求的目的以創造更大的利益價值。此研究中所提出的需求鏈管理本體論呈現企業回應顧客需求過程中所需的多層級知識架構及其關聯性,開發團隊可以透過代理人系統整合獲取所求的資訊,並且應用在研發與生產階段,以便可以及早回應。
研究中的案例主要是探討如何以本體論為基之多代理人建構的系統雛型來促進彼此之間的知識分享及應用將需求知識,以便合作團隊易於找尋所需要的資料,澄清與滿足顧客需求、降低成本及前置時間;也根據案例需求設定雛型中的代理人的特質如溝通規則、行為和決策等,希望能幫助需求鏈裡的上下游公司的溝通與合作。

In the fast changing market environment, Demand Chain Management (DCM) has been considered as a powerful strategy to increase enterprise competency. This practice enables enterprises to get the latest market demand information for further product and process development to meet market needs in the early stage of development
This research explores the issue of how ontology-based Multi-Agent System (MAS) help to facilitate the communication of demand information among IC design service companies. MAS is expected to enable relevant agents participating team members from different companies to coordinate and communicate based on the roles and rules specified in the ontology. In this research, agents are designed to represent department or person in-charge for quick response without waiting for instruction.
The specific roles and rules for upstream and downstream partners in demand chain enable them to have systematic interaction to evaluate and balance the supply-demand conditions in order to create customer value. The DCM ontology in this research needs to represent multi-level knowledge structure and relationship among partners in demand chain for the partners to acquire needed knowledge through MAS in the development and production stage.
Specifically, the case study in this research demonstrates the demand knowledge sharing among the agents in systematic format such as communication protocol, behavior and decision patterns in order to help the key performance in DCM such as: meet customer demand, reduced development cost and lead time.
Contents
摘要 I
Abstract II
Acknowledgement III
Contents IV
Figure Contents VII
Table Contents VIII
Chapter 1 Introduction 1
1.1 Background 1
1.2 Issues 1
1.3 Objectives 2
1.4 Research Scope 3
Chapter 2 Literature Review 4
2.1 Demand Chain Management (DCM) 4
2.1.1 Demand Chain Management Definition 4
2.1.2 DCM Conceptual Framework 5
2.1.3 DCM Model 6
2.1.4 Information Flow within DCM 7
2.2 Overview of Ontology 8
2.2.1 Ontology Definition 8
2.2.2 The Ontology Development 9
2.2.3 Why Develop an Ontology? 11
2.2.4 DCM Ontology .12
2.3 Agent System 15
2.3.1 Multi-Agent System (MAS) 15
2.3.2 Agent Communication Languages (ACLs) 17
2.3.3 Agent-based Framework for Collaborative Product Development 19
2.3.4 Ontology for Agent 19
2.3.5 Ontology-based Multi-Agent System for DCM 21
Chapter 3 Research Methodology 23
3.1 Research Methodology 23
3.2 Research Model 25
3.3 System Requirement and Software Tool 26
3.3.1 System Requirement 26
3.3.2 Software Tool Introduction 27
3.3.3 Software Tool Selection 28
3.4 Ontology-based MAS Framework 29
3.4.1 Company Types and Agents 29
3.4.2 Ontology-based MAS Framework 30
3.4.3 Main Agents Functions Description 32
3.4.4 Communication Model 36
3.5 Case Company Selection 36
3.5.1 Company Background 38
3.5.2 Case Study Description 40
3.5.3 Case Study Analysis- Diamond Standard 108 Mini Processor 40
3.5.4 Diamond Standard 108 Mini Processor DCM Ontology 42
Chapter 4 System Analysis and Discussion 44
4.1 System Structure and Design 44
4.1.1 System Structure Description 44
4.1.2 System Structure Construction 45
4.1.3 System Hypothesis 47
4.2 System Tool of Ontology-based MAS Framework 48
4.2.1 Knowledge Type and Ontology of the Case Scenario 49
4.2.2 FIPA-Ontology for Agent Communicatio 51
4.2.3 Information Flow between Agents in the Case Scenario 53
4.2.4 Ontology-based MAS in Different Stages of Case Scenario 54
4.2.5 Variables Setting 56
4.2.6 Interface Example for Case Scenario 58
4.2.7 Constraints and Rules 60
4.2.8 Reasoning Process In Ontology-based MAS to Support DCM 65
4.2.9 Generation of New Knowledge in the Ontology 68
4.3 Agent Behavior in Ontology-based MAS 70
4.4 Comparison of the Level of Specificity 72
4.5 Discussion 74
Chapter 5 Conclusion and Future 76
5.1 Conclusion 76
5.2 Future Work 77
Reference 79
中文文獻 83
Websites 84
Appendix A Description of Case Study 2 85
Appendix B Ontology-based MAS in Different Stages of Case Scenario 89





















Figure Contents

Figure 2-1: DCM Conceptual Framework 5
Figure 2-2: DCM Model 7
Figure 2-3: An Example of Ontology 9
Figure 2-4: The Ontology Development Process 10
Figure 2-5: DCM Core Knowledge 12
Figure 2-6: DCM Ontology 14
Figure 2-7: Diamond Standard 108 Mini Processor DCM Ontology 15
Figure 3-1: Research Procedure 24
Figure 3-2: Research Model for Building Ontology-based MAS for DCM 26
Figure 3-3: An Example of Ontology-based MAS Framework 31
Figure 3-4: Communication and Coordination within and between Model 37
Figure 3-5: Diamond Standard 108 Mini Processor DCM Ontology 43
Figure 4-1: Interrelationship between Agents from Case Companies 45
Figure 4-2: System Structure 46
Figure 4-3: System Tool of Ontology-based MAS Framework 48
Figure 4-4: Information Flow between Agents 61
Figure 4-5: Interface for Procurement Agent to Key in Requirement 54
Figure 4-6: The Constraints and Rules of the Prototype Graph 61
Figure 4-7: Reasoning Process in Ontology-based MAS to Support DCM 66
Figure 4-8: Generation of New Knowledge Process 69
Figure 4-9: New Knowledge Creation in the Ontology 70
Figure 4-10: Process Flow of Ontology-based MAS to Support DCM 73








Table Contents

Table 2-1: Ontology Definition 8
Table 2-2: DCM Knowledge Type 14
Table 3-1: Comparison between JADE and Aglets 29
Table 3-2: Company Types and Agents 30
Table 3-3: Selected Case Company 38
Table 3-4: Case Study and Companies 40
Table 4-1: Case Company and Represented Agents 44
Table 4-2: FIPA-Ontology for the System 52
Table 4-3: Stage 1- Sending Demand Inquiry 55
Table 4-4: Variables Setting 57
Table 4-5: Main Variables in the System 58
Table 4-6: Agent Action Variables in the System 58
Table 4-7: The Meaning of the Constraints and Rules in the System 62
Table 4-8: Constraints of the Prototype 63
Table 4-9: Rules of the Prototype 63
Table 4-10: Constraint and Rules in the Example of Reasoning Process 68
Table A-1: Case Study, Companies and Agents 86
Table A-2: Comparison of Case 1 & Case 2 88
Table B-1: Stage 2- Asking to Upgrade Module 89
Table B-2: Stage 3-Asking to Change Design 90
Table B-3: Stage 4-Sending Demand Inquiry 91

Figure Contents
Figure 2-1: DCM Conceptual Framework 5
Figure 2-2: DCM Model 7
Figure 2-3: An Example of Ontology 9
Figure 2-4: The Ontology Development Process 10
Figure 2-5: DCM Core Knowledge 12
Figure 2-6: DCM Ontology 14
Figure 2-7: Diamond Standard 108 Mini Processor DCM Ontology 15
Figure 3-1: Research Procedure 24
Figure 3-2: Research Model for Building Ontology-based MAS for DCM 26
Figure 3-3: An Example of Ontology-based MAS Framework 31
Figure 3-4: Communication and Coordination within and between Model 37
Figure 3-5: Diamond Standard 108 Mini Processor DCM Ontology 43
Figure 4-1: Interrelationship between Agents from Case Companies 45
Figure 4-2: System Structure 46
Figure 4-3: System Tool of Ontology-based MAS Framework 48
Figure 4-4: Information Flow between Agents 61
Figure 4-5: Interface for Procurement Agent to Key in Requirement 54
Figure 4-6: The Constraints and Rules of the Prototype Graph 61
Figure 4-7: Reasoning Process in Ontology-based MAS to Support DCM 66
Figure 4-8: Generation of New Knowledge Process 69
Figure 4-9: New Knowledge Creation in the Ontology 70
Figure 4-10: Process Flow of Ontology-based MAS to Support DCM 73

Table Contents
Table 2-1: Ontology Definition 8
Table 2-2: DCM Knowledge Type 14
Table 3-1: Comparison between JADE and Aglets 29
Table 3-2: Company Types and Agents 30
Table 3-3: Selected Case Company 38
Table 3-4: Case Study and Companies 40
Table 4-1: Case Company and Represented Agents 44
Table 4-2: FIPA-Ontology for the System 52
Table 4-3: Stage 1- Sending Demand Inquiry 55
Table 4-4: Variables Setting 57
Table 4-5: Main Variables in the System 58
Table 4-6: Agent Action Variables in the System 58
Table 4-7: The Meaning of the Constraints and Rules in the System 62
Table 4-8: Constraints of the Prototype 63
Table 4-9: Rules of the Prototype 63
Table 4-10: Constraint and Rules in the Example of Reasoning Process 68
Table A-1: Case Study, Companies and Agents 86
Table A-2: Comparison of Case 1 & Case 2 88
Table B-1: Stage 2- Asking to Upgrade Module 89
Table B-2: Stage 3-Asking to Change Design 90
Table B-3: Stage 4-Sending Demand Inquiry 91
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中文文獻
1. 鄭雅萍,2007,「以需求鏈管理之知識本體論促進企業營運敏捷度」,國立清華大學工業工程與工程管理研究所碩士論文。
2. 簡嘉建,2003,「建構在語意網上之分散式電子化學習物件分享機制:以「物料需求規劃」之學習為例」,國立清華大學工業工程與工程管理研究所碩士論文。
3. 吳誌恭,2002,「供應鏈夥伴間知識網路之協調合作模式-以IC產業上游供應商之業務與下游顧客之採購為例」,國立清華大學工業工程與工程管理研究所碩士論文。
4. 洪勖芳,2002,「全球化協同式產品開發之知識整合與智產管理-以系統單晶片產品開發為例」,國立清華大學工業工程與工程管理研究所碩士論文。
5. 莊宗文,2002,「運用三維同步工程及知識網路之整合性產品開發」,國立清華大學工業工程與工程管理研究所碩士論文。
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