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

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研究生：	李珮晶 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 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 Yee-Yeen Chu
口試委員:
學位類別：	碩士 Master
系所名稱：	工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management
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), Ontology, Multi-Agent System (MAS)
相關次數：	點閱：3 下載：0
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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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