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中文論文名稱 廢棄物回收之逆物流網路設計與運送規劃-以廢電腦為例
英文論文名稱 Reverse Logistics Network Design and Planning for Waste Recycling- A Case Study on Waste Computer Recycling
校院名稱 淡江大學
系所名稱(中) 運輸管理學系碩士班
系所名稱(英) Department of Transportation Management
學年度 97
學期 2
出版年 98
研究生中文姓名 謝佳蓉
研究生英文姓名 Chia-Jung Hsieh
學號 696660330
學位類別 碩士
語文別 中文
口試日期 2009-06-18
論文頁數 113頁
口試委員 指導教授-溫裕弘
委員-王中允
委員-時序時
中文關鍵字 綠色逆物流  逆物流網路結構設計  逆物流網路運送規劃  廢棄物回收 
英文關鍵字 Green Reverse Logistics  Reverse Logistics Network structure Design  Reverse Logistics Network Planning  Waste Recycling 
學科別分類 學科別社會科學管理學
中文摘要 綠色逆物流為綠色供應鏈最重要的環節之一,其中,廢棄物回收逆物流常伴隨有害特性,影響層面廣,逆物流回收網路問題複雜並漸趨重要,逆物流網路規劃必須考慮逆物流中心的相關區位、市場範圍與影響、逆物流運送之最適規劃。援此,本研究嘗試探討一系列逆物流網路規劃模式,包括區位選擇與網路運送規劃。
本研究建構廢棄物回收之逆物流網路結構設計與運送規劃模式,分為灰色聚類與數學規劃兩階段。灰色聚類用以進行逆物流區位選擇,包含回收廠、拆解/處理廠、再生廠和最終處理廠的地點設置,而本研究之聚類指標為運輸成本、固定成本和人口曝露風險。另一階段為數學規劃基礎之逆物流網路運送規劃模式,目標函數以總逆物流成本以及總逆物流風險最小作為考量,其中總逆物流成本包含有運輸成本、營運成本以及處理成本,總逆物流風險則為運送風險與儲存風險。另外,限制式包含考慮流量守恆限制、設施容量限制、設施數量限制、環保法規、非負限制等相關限制。最後,本研究以廢棄電腦回收為例,以驗證模式之可行性。研究結果顯示,廢棄物回收之逆物流網路結構設計結果與實務現況比較,驗證結果合理;而逆物流運送規劃結果亦可行。由於模式中所使用的參數具高度不確定性,而為了解當參數變化對於輸出所造成的影響,亦進行敏感度分析,其結果可提供逆物流運送規劃上更具決策彈性。
本研究探討逆物流網路設計與運送規劃模式,目的在於因應企業導入綠色供應鏈概念,輔助相關第三方逆物流運送業者在複雜、多變之逆物流業務流程下,藉由不同的考量,使企業在發展逆物流業務具決策基礎與決策彈性,並提供綠色供應鏈之策略規劃與決策支援的參考。
英文摘要 Green reverse logistics is one of the most important issues in green supply chain management. Reverse logistics is referred to as the process of logistics management involved in planning, managing, and controlling the flow of wastes for either reuse or final disposal of wastes. However, waste recycling often includes hazardous characteristics. Hazardous-waste reverse logistics may be useful for solving waste-induced environmental pollution problems that accompany high-technology industrial development. Reverse logistics network design for waste recycling encompasses decisions on reverse logistics network shape, the topological relationships among reverse logistics centers, and reverse distribution programming. This study developed a series of models to design a network structure of waste reverse logistics and to determine distribution flow for waste recycling.
In the first part of this study, the reverse logistics network shape is designed and formed into a network structure by applying grey clustering. This study defines transportation cost index and risk index. The topological relationships and locations among reverse logistics centers are selected. Using grey clustering, the recycling plants, disassembling plants, recycling companies and the final treatment plants are chosen and determined. In the second part of the study, on the basis of the designed network shape (structure), this study proposes a mathematical programming model to determine the optimal distribution flows on all of the links forming the designed reverse logistics network for waste recycling. The main objective function is to minimize the total cost of reverse logistics as well as the total risk. The total cost includes transportation cost, operating cost and disposal cost. On the other hand, the total risk includes enroute holding risk and the site stock risk. Furthermore, the model is also subjected to flow conservation, capacity of facilities, limit amount of facilities, the environmental regulations, and non-negative constraints. Finally, a case study with a waste computer recycling is provided to illustrate the results and the application of the models. Sensitivity analysis is also discussed. The results of the case study verify that the models are practicable, and also provide higher flexibility on decision-making for reverse logistics services providers.
This study demonstrates how grey clustering and mathematical programming might be applied to the reverse logistics network design problems and discusses many issues in waste recycling reverse logistics. In addition, it is envisaged that the results of this study may shed light on strategic and operational planning related for waste reverse logistics service providers.
論文目次 目錄
中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
圖目錄 vii
表目錄 ix

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3研究範圍 7
1.4研究架構與流程 7
第二章 文獻回顧 11
2.1 綠色供應鏈逆物流管理 11
2.2 逆物流規劃 12
2.3有害廢棄物 17
2.4 逆物流相關成本 23
2.5 逆物流風險量化評估 23
第三章 逆物流網路結構設計與運送規劃模式 27
3.1 逆物流網路模式架構 27
3.2逆物流網路結構設計 31
3.2.1 灰色聚類法 35
3.2.2 聚類指標 38
3.2.3 白化權函數 41
3.2.4灰色聚類決策流程 45
3.3逆物流網路運送規劃模式 46
3.3.1 逆物流網路節點、節線流量定義 46
3.3.2成本函數與風險函數 50
第四章 範例分析 60
4.1 逆物流網路結構設計之範例分析 60
4.1.1第一階層的區位選擇 60
4.1.2第二階層的區位選擇 66
4.2逆物流運送規劃之範例分析 83
4.2.1 資料說明 83
4.2.2模式求解 89
4.2.3模式敏感度分析 94
第五章 結論與建議 102
5.1結論 102
5.1.1逆物流網路結構設計 102
5.1.2逆物流網路運送規劃 103
5.2建議 105
參考文獻 106

圖目錄
圖1.1 一般終止產品的處理程序 3
圖1.2 逆物流網路示意圖 4
圖1.3 研究架構圖 9
圖1.4 研究流程圖 10
圖2.1 綠色供應鏈作業分類 13
圖2.2 廢資訊品處理流程 22
圖3.1 兩階段之逆物流網路模式架構 28
圖3.2 本研究逆物流網路建構程序 29
圖3.3 逆物流網路活動的基本流向關係 33
圖3.5 逆物流網路節點集合示意圖 34
圖3.6 灰色聚類應用於逆物流網路結構之基本程序 37
圖3.7 與 之關係 40
圖3.8 指標 灰類 「低於等於某數」、「大約等於某數」、「高於等於某數」三類型白化權函數 44
圖3.9 與 之間的關係示意圖 48
圖3.10 、 和 之間的關係示意圖 48
圖3.11 、 、 與 之間的關係示意圖 49
圖3.12 與 之間的關係示意圖 49
圖3.13 與 之間的關係示意圖 50
圖3.14 廠址之人口曝露影響 55
圖4.1 範例分析範圍-北臺灣候選節點 61
圖4.2 回收廠之總成本指標對應低、中、高成本三類型灰類的白化權函數 63
圖4.3 初步逆物流網路結構設計 64
圖4.4 拆解/處理廠的總成本指標對應低、中、高成本三類型灰類的白化權函數 67
圖4.6 最終處理廠之總成本指標對應低、中、高成本三類型灰類的白化權函數 71
圖4.7 拆解/處理廠的總風險指標對應低、中、高風險三類型 73
灰類的白化權函數 73
圖4.8 再生廠的總風險指標對應低、中、高風險三類型灰類的白化權函數 75
圖4.9 最終處理廠的總風險指標對應低、中、高風險三類型灰類的白化權函數 77
圖4.10 逆物流網路結構設計示意圖 82
圖4.11 營運成本之變動對總目標值變動幅度的影響 97
圖4.12 營運成本之變動對總成本變動幅度的影響 97
圖4.13 營運成本之變動對總風險變動幅度的影響 98
表目錄
表2.1 逆向物流與正向物流的比較 14
表2.2 逆物流網路規劃之文獻整理 18
表2.3 逆物流項目 19
表2.4 不同清理階段之混合五金廢料認定對照表 21
表2.5 逆物流網路規劃文獻之成本函數整理 24
表2.6 一般風險的定義 25
表2.7 有害廢棄物風險的之評估方法比較表 26
表3.1 本研究灰色聚類之內容比較 36
表4.1 初步候選區位計算之結果 65
表4.2 選定為拆解/處理廠之計算結果 79
表4.3 選定為再生廠之計算結果 80
表4.4 選定為最終處理廠之計算結果 81
表4.5 96年廢資訊產品(含通信用品) 84
表4.6 廢資訊物品回收清除處理補貼費 87
表4.8 逆物流運送規劃之整體結果 90
表4.9 逆物流運送規劃之運送情形 91
表4.9 逆物流運送規劃之運送情形(續) 92
表4.9 逆物流運送規劃之運送情形(續) 93
表4.10 變化後之各參數(一般處理作業) 95
表4.11 變化後之各參數(低風險妥善的處理作業) 95
表4.12 廠址之營運成本對總成本的影響 96
表4.13 敏感度分析結果 101


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