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研究生:劉育誠
研究生(外文):Liu, Yu-Cheng
論文名稱:利用差距分析法(GBM)以兩階段流程求解網路結構之資料包絡分析(NDEA)績效評量
論文名稱(外文):A two-phase procedure employing Gap Based Measure (GBM) for solving Network Data Envelopment Analysis (NDEA)
指導教授:劉復華劉復華引用關係
指導教授(外文):Liu, Fuh-hwa Franklin
口試委員:林則孟許棟樑彭文理陳勝一
口試委員(外文):Lin, James T.Sheu, Dong-LiangPearn, Wen LeaChen, Sheng-I
口試日期:2018-06-26
學位類別:博士
校院名稱:國立交通大學
系所名稱:工業工程與管理系所
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:2018
畢業學年度:106
語文別:英文
論文頁數:75
中文關鍵詞:網路DEA資料包絡分析投入/產出分析動態網路效率評估
外文關鍵詞:Network DEAData envelopment analysisInput/output analysisDynamic networkEfficiency measurement
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資料包絡分析 (data envelopment analysis, DEA) 為互評多個生產單位的績效,各單位以多項投入轉換成多項產出的製程,以較少的投入且較多的產出,來改善生產效率。網路型DEA則有網路結構的多項製程,各製程有多項外部的投入與產出項目,每兩製程 (節點)間有多項中間項目 (鏈結)。本論文應用了差距分析法之數學規劃模型 (gap-based measure, GBM-bc),制定其各投入、鏈結與產出項目的影子價格 (shadow price) 及影子差額 (shadow slack),以評估該生產單位在群體中之相對效率。本研究建立一個新的混合整數數學規劃模型,每個鏈結項目以一個二元整數決策變數 (binary integer decision variable) 表示,其可區辨增加或減少。第一階段分析結果,決定了每個鏈結項目屬於類投入項目 (analog-as-input) 以求取降低差額或是屬於類產出項目 (analog-as-output) 以求取增加差額。第二階段的分析,再以整體製程系統而言,直接利用GBM-bc評量每個生產單位時,制定其各投入、類投入、產出與類產出項目的影子價格 (shadow price) 及影子差額 (shadow slack),以評估該生產單位在群體中之相對效率。本論文以兩製程的小型網路結構說明鏈結的數學模型之內涵,另網路結構型式多製程之數學模型亦提出以資引用。本論文除了新模型的架構及介紹外,也以實際的供應鏈管理實例說明如何應用此模型來評核整體供應鏈的績效.
Data envelopment analysis (DEA) models are used to measure the relative efficiency of each decision-making unit (DMU) against its peers regarding multiple input indices vs. multiple output indices. Fewer input values and more output values are desired to improve its aggregated performance score. This thesis adopts a binary integer linear programming model to construct a mixed integer program to solve Two-stage and Network DEA problems. It sets the total numbers of ‘analog-as-input’ (aa-input) and ‘analog-as-output’ (aa-output) equal to the total number of links in the objective function, the links are played either ‘aa-input’ or ‘aa-output’. This thesis proposes a two-phase procedure to solve the problem. The main objective of Phase-I is partitioning the bundle of intermediate links into two bundles of aa-inputs and aa-outputs. Phase-II is to determine the shadow prices as well as shadow slacks of inputs, outputs, aa-inputs and aa-outputs to obtain the best-practice efficiency. Input and as-input measures reduce slack, whereas output and as-output measures increase slacks to attain their target on the production frontier. Besides the model introduction, this thesis also applies this model to measure the aggregated efficiency of the real world supply chain management.
摘要 I
Abstract II
誌謝 III
Contents IV
List of Figures VI
List of Tables VII
Symbols VIII
1. Introduction 1
1.1. The models adopted in this manuscript 2
1.2. The purpose and innovation of this manuscript 3
1.3. Apply this model in supply chain management 4
1.4. Organization of the dissertation 5
2. Literature review 7
2.1. DEA framework 7
2.1.1. CCR 7
2.1.2. SBM 9
2.1.3. GBM-bc 10
2.2. Two-stage and Network DEA 14
3. Gap-based Measure (GBM-bc) Two-stage DEA model 16
3.1. The calculation procedure of Phase-I 17
3.2. The calculation procedure of Phase-II 19
3.3. The solution of Two-stage GBM-bc model 22
3.4. Numerical example-the dataset 23
3.5. The conclusions of Two-stage approach 23
3.6. Analysis of numerical example and managerial insights 28
4. Gap-based Measure (GBM-bc) Network DEA model 30
4.1. Network GBM-bc model 31
4.1.1. The calculation procedure of Phase-I 31
4.1.2. The calculation procedure of Phase-II 33
4.1.3. Sub-process efficiencies 36
4.1.4. Virtual-gap diagram 36
4.2. Numerical example-Network GBM-bc model 37
4.2.1. The proposed GBM-bc model 37
4.2.2. Analysis of numerical example 39
4.2.3. Managerial insights 44
5. A methodology to assess the supply chain performance based on GBM-bc 45
5.1. Supply chain management process and motivation 50
5.1.1. Stage I – Plan Source 50
5.1.1.1. Supplier performance 50
5.1.1.2. Material planning strategy 50
5.1.2. Stage II – Plan Make 52
5.1.2.1. MRP-type: Push system 52
5.1.2.2. JIT-Type: Pull system 52
5.2. Models description 53
5.3. Case application 56
5.3.1. Key performance indices 57
5.3.2. Analysis of calculation procedure 60
5.3.3. Managerial insights 64
6. Conclusions 70
Reference 72
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