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研究生: 方耀利
Aulia - Rahman Mufti Fikri
論文名稱: 具適應性類神經模糊推論系統學習之協商式產能規劃模型
Negotiation-Based Capacity Planning with a Learning Mechanism Using Adaptive Neuro-Fuzzy Inference System
指導教授: 王孔政
Kung-Jeng Wang
口試委員: 林希偉
Shi-Woei Lin
曹譽鐘
Vincent F.Yu
學位類別: 碩士
Master
系所名稱: 管理學院 - 工業管理系
Department of Industrial Management
論文出版年: 2016
畢業學年度: 104
語文別: 英文
論文頁數: 38
中文關鍵詞: 自動協商產能規劃學習機制協商決策函數
外文關鍵詞: Automated negotiation, capacity planning, learning mechanism, negotiation decision function
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在一有多個工廠散布於各地的分散式製造環境當中,生產系統的複雜性相對提升,而產能規劃與資源分配議題,因會影響整個系統的表現,也越來越受到關注。本研究發展一整合式架構進行多廠區環境在有限資金下的配送。本研究建構依協商架構結合學習機制,進行自主配送總部所提供的有限資金,以及促進散布於單一廠區的有限製造資源的使用。由實驗顯示在與競爭對手協商時能有好的預測結果,因此證明本研究能有效縮短協商的時間。


In decentralized manufacturing environment with multiple factories that are scattered geographically, the complexity of production systems increases, and capacity planning and allocation of resources have become a significant concern that affects system performances. This study focuses on the development of an integrated framework to allocate limited budget in a multiple-factory environment. We develop a negotiation framework with learning mechanism to allocate autonomously finite budget provided by a headquarter and to facilitate the use of limited manufacturing resources that are scattered over individual factories. The outcome of the experiments shows good prediction of the opponent offers during negotiation, so it enables the reduction of negotiation time.

ABSTRACT I 摘要 II Acknowledgement III Table of Contents IV List of Figures VI List of Tables VII Chapter 1 INTRODUCTION 1 1.1 Research Background and Motivation 1 1.2 Research Objective and Procedure 2 1.3 Thesis Organization 3 Chapter 2 LITERATURE REVIEW 4 2.1 Automated Negotiation with Learning Mechanism 4 2.2 Negotiation Decision Functions 5 2.3 Solving Stochastic Capacity Planning Problem using Genetic Algorithm (GA) 6 Chapter 3 MATHEMATICAL MODEL AND METHOD 8 3.1 Problem Formulation 8 3.1.1 Negotiation Model for Resource Planning among Factories 8 3.1.2 Time-Dependent Tactic based on Negotiation Decision Function (NDF) 10 3.1.3 Capacity Planning Model of Individual Factories 12 3.2 Learning Mechanism using Adaptive Neuro-Fuzzy Inference System (ANFIS) 18 Chapter 4 EXPERIMENTS RESULTS 22 4.1 Solving Local Capacity Planning Model using Genetic Algorithm 22 4.2 Predicting Opponent Offer by utilizing Adaptive Neuro-Fuzzy Inference System (ANFIS) as Learning Mechanism 29 Chapter 5 CONCLUSIONS AND FUTURE RESEARCH 33 5.1 Conclusions 33 5.2 Future Research 33 REFERENCES 35 Appendix A: Parameter setting 37

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