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研究生: 蕭煥琳
HUAN-LIN SIAO
論文名稱: 應用模糊推論系統衡量餐廳服務網路之可靠度
Using Fuzzy Inference System Evaluate the Restaurant Service Network Reliability
指導教授: 林義貴
Yi-Kuei Lin
口試委員: 王孔政
Kung-Jeng Wang
曹銳勤
Ruey-Chyn Tsaur
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: 2013
畢業學年度: 101
語文別: 英文
論文頁數: 111
中文關鍵詞: 模糊推論系統語意變數蒙地卡羅方法餐廳服務網路工作研究
外文關鍵詞: Fuzzy inference system, Linguistic variable, Monte Carlo Method, Restaurant service network, Work study
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  • 真實世界中的許多系統,可將其複雜的架構以邊(arc)及節點(node)建置成網路模型,網路模型可以被廣泛的應用在現實的生活中,如服務網路、通信網路、物流網路、電腦網路與電力網路等,並透過分析網路模型之流量的方法評估其績效,網路可靠度就是衡量網路的一個重要的指標。服務網路主要由人構成而具有人性的因素,難以去衡量服務系統內人員產能之分布,因此導入了模糊推論系統(Fuzzy inference system)以衡量之,進而幫助服務系統可靠度的評估。在服務網路中流量在不同的階段因單位不同的問題,使用minimal paths或minimal cuts求網路可靠度之方法並不可行。因此,本研究以蒙地卡羅方法(Monte Carlo Methods)去模擬服務網路流量值,並透過實際訪察日式料理餐廳,根據服務流程以及日式料理餐廳相關資訊建立日式餐廳服務網路(Japanese restaurant service network),以時間研究(time study)方法計算餐廳員工服務客人的標準工時,進而計算員工之產能範圍。模糊推論系統之語意變數(linguistic variable)的取得,則是透過訪問經理以及廚房大廚以及在工作研究中發現,待模糊推論系統建構完成後推論出在考量各種因素下之員工產能狀態。本文的目標是要衡量餐廳服務系統一小時可以滿足來客數(demand)之可靠度,餐廳經理以可靠度為一指標來衡量餐廳之服務水準。


    Many real-world systems can be built into a network using arcs and nodes. A network model can be widely used in real life, such as in service networks, logistics networks, computer networks, and electricity networks, and we can therefore further evaluate the performance by analyzing the network flow. System reliability is an important network performance index. The network flow and the distribution of the capacity should be study before calculating the system reliability. A service network is mainly constructed by people and work places, and it is difficult to measure the distribution of the capacity of each employee based on human factors. Therefore, importing a fuzzy inference system to measure the value of a capacity can help in the assessment of service system reliability. In addition, minimal paths cuts cannot be applied to evaluate the reliability, and we therefore utilize the Monte Carlo method to simulate the flow. A Japanese restaurant is used as a case study in this thesis. Japanese restaurant service network (JRSN) is constructed based on the service process and the related information about the Japanese restaurant. The linguistic variables are obtained through interviews with the managers and chefs, or are found through a work study. The capacity states of JRSN are inferred by considering various factors after the fuzzy inference system is completed. The goal is to evaluate the reliability of JRSN under d customers per hour. The manager of the restaurant uses the reliability to evaluate the service quality of the restaurant.

    摘要 I ABSTRACT II ACKNOWLEDGMENTS III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VIII Chapter 1 INTRODUCTION 1 1.1. Background and motivation 1 1.2. Research objectives 2 1.3. Overview of this thesis 3 Chapter 2 LITERATURE REVIEW 5 2.1. Monte Carlo Method 5 2.2. Fuzzy theory 6 2.3. Network reliability analysis 7 2.4. Network reliability for the assignment 9 Chapter 3 PROBLEM MODELING AND FLOW ANALYSIS 11 3.1. Service system modeling 12 3.2. An example for RSN 14 3.3. The capacity of each employee 16 3.3.1. The formulation of capacity 17 3.3.2 A Numerical example for the capacity of each employee 19 3.4. The flow in RSN 20 3.4.1. The flow of an RSN 20 3.4.2. A numerical demonstration of a flow in phase III of the RSN 24 Chapter 4 THE CAPACITY OF RSN MEMBER GAINED FROM THE FUZZY INFERENCE SYSTEM AND RSN RELIABILITY 27 4.1. Linguistic variable 28 4.2. Operation on Fuzzy sets 30 4.2.1. S-norms and T-norms 30 4.2.2. Fuzzy relation 31 4.2.3. Projection 31 4.3. Fuzzy rule base 31 4.4. Fuzzy inference system 34 4.4.1.Fuzzy logic with generalized modus ponens 35 4.4.2. Fuzzy inference system with center average defuzzifier 36 4.5. System capacity state of RSN 37 4.5.1. Algorithm for the capacity state 37 4.5.2. A numerical demonstration of the capacity state 39 4.6. RSN Reliability 43 Chapter 5 CASE-BASED EXPERIMENTS 45 5.1. Model building for JRSN 45 5.2. Capacity Study 48 5.3. Linguistic variable of Hachioji 51 5.4. RSN reliability 53 Chapter 6 CONCLUSIONS AND FUTURE RESEARCH 57 6.1. Conclusions 57 6.2 Future research 57 REFERENCES 61 APPENDIX A. Linguistic variables of chefs 66 APPENDIX B. Linguistic variables of waitresses 67 APPENDIX C. The fuzzy set of chefs 68 APPENDIX D. The fuzzy set of waitresses 71 APPENDIX E. Fuzzy rules base for the chef 72 APPENDIX F. Fuzzy rules base for the waitress 98

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