因應全球航空貨運的發展，航空公司間建立航空貨運聯盟成為重要策略之一。本研
究探討航空貨運聯盟之聯盟組合與營收分配問題，考慮航空公司在貨運聯盟協商階段，
對於貨運聯盟協定所衍生的營收預期效果、營收分攤均具有高度不確定性，而聯盟協定
之營收分配結果又直接影響聯盟成員航空公司貨運航線容量分配與控制規劃，藉此進行
航空貨運聯盟組合及營收分配之決策。本研究建立一航空貨運聯盟之營收分配模式，探
討航空貨運在聯盟前後狀態下之營收變化，利用模糊合作賽局求解航空貨運聯盟內航空
公司貨運之航線組合及營收分配，以作為航空公司貨運聯盟協商決策之基礎。
  本研究以天合聯盟貨運內之航空公司為對象進行數值範例分析，在貨運聯盟營收分
配結果顯示，整體聯盟最佳化下，貨運聯盟合作之經濟效益為正面的;然而，因為考慮
到合作對象之組合時，勢必有航空公司未與其他聯盟成員因聯盟合作後，影響自身之營
收，此外，航空公司所在的樞紐機場亦會影響與其他航空公司之合作。綜上，本研究成
果提供在學術上做為航空貨運聯盟組合及營收分配之建模基礎，在實務上亦可提供協商
階段之決策參考。

Entering airline cargo alliance has become one of the major strategies for air cargo
carriers in a competitive environment. This study discusses the airline cargo alliance decision-making problems and determines the coalition and revenue sharing under air cargo alliances. Members of the airline cargo alliance negotiate the terms of the agreements they will use to split the revenue of interline and codeshare routes. A fuzzy cooperative game is developed to model the output of the negotiation in which airlines decide the proration rules. This study uses a fuzzy cooperative game to assess the coalition of allied airline cargo members and routes, and the revenue sharing proration rate is determined. A numerical case study will demonstrate the synergy benefits of airline cargo alliances and the feasibility of applying the proposed models. The results of this study explore the effect of airline cargo alliances on revenue sharing and coalitions. In addition, it is envisaged that the results of this study may shed light on airline cargo alliance strategies.

目錄
第一章	緒論	1
1.1研究背景與動機	1
1.2研究目的	7
1.3研究範圍	8
1.4研究流程	9
第二章	文獻回顧	11
2.1航空公司策略聯盟、貨運聯盟相關文獻	11
2.2航空貨運營收管理相關文獻	14
2.3模糊理論相關文獻	16
2.4合作賽局相關文獻	19
2.5小結	20
第三章	模式建構	21
3.1基本假設與模式型式	21
3.2夏普利值之求解	22
3.3模糊合作賽局之航空貨運聯盟營收分配模式	25
3.4模式求解步驟	29
第四章	數值範例	32
4.1範例數值設定	32
4.2模糊合作賽局之貨運聯盟組合	33
4.3 TPE-JFK航線起迄對範例	35
4.4 TPE-CDG航線起迄對範例	48
4.5 PVG-AMS航線起迄對範例	59
第五章	結論與建議	67
5.1結論	67
5.2建議	69
參考文獻		71
中文部分	71
英文部分	73

 
表目錄
表2.1航空公司策略聯盟、貨運聯盟相關文獻相關文獻彙整表	13
表2.2航空公司貨運營收管理相關文獻彙整表	16
表3.1聯盟及對應報酬	23
表3.2邊際貢獻	24
表4.1 TPE-JFK航空公司航線組合	35
表4.2 TPE-JFK模糊營收區間值	38
表4.3 TPE-JFK整體貨運聯盟組合	41
表4.4 TPE-JFK不同信賴水平上夏普利值區間	43
表4.5 TPE-JFK聯盟前後營收變化	46
表4.6 TPE-CDG航空公司航線組合	48
表4.7 TPE-CDG模糊營收區間值	50
表4.8 TPE-CDG整體貨運聯盟組合	53
表4.9 TPE-CDG不同信賴水平上夏普利值區間	54
表4.10 TPE-CDG聯盟前後營收變化	58
表4.11 PVG-AMS航空公司航線組合	59
表4.12 PVG-AMS模糊營收區間值	60
表4.13 PVG-AMS整體貨運聯盟組合	61
表4.14 PVG-AMS不同信賴水平上夏普利值區間	62
表4.15 PVG-AMS聯盟前後營收變化	65
圖目錄
圖 1.1 Kasilingam航空貨運營收管理模式	4
圖1.2研究流程圖	10
圖2.1三角形模糊數	17
圖2.2梯形模糊數	18
圖2.3鐘形模糊數	18
圖2.4不規則模糊數	18
圖3.1聯盟營收分配求解架構	31
圖4.1 TPE-JFK參賽者1夏普利值隸屬函數	44
圖4.2 TPE-JFK參賽者2夏普利值隸屬函數	44
圖4.3 TPE-JFK參賽者3夏普利值隸屬函數	45
圖4.4 TPE-JFK參賽者4夏普利值隸屬函數	45
圖4.5 TPE-CDG參賽者1夏普利值隸屬函數	55
圖4.6 TPE-CDG參賽者2夏普利值隸屬函數	55
圖4.7 TPE-CDG參賽者3夏普利值隸屬函數	56
圖4.8 TPE-CDG參賽者4夏普利值隸屬函數	56
圖4.9 PVG-AMS參賽者1夏普利值隸屬函數	63
圖4.10 PVG-AMS參賽者3夏普利值隸屬函數	63
圖4.11 PVG-AMS參賽者4夏普利值隸屬函數	64

中文部分
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