貨櫃拖曳車輛的排程不僅需要規劃路線，還必須考量拖車拖曳貨櫃的數量限制，此問題與一般車輛途程問題明顯不同。近年來疫情復甦，機場地勤需求增加，使得此問題的規模變得更加複雜。若從系統最佳化的角度，透過電腦運算，所獲得的規劃結果不僅能夠縮短貨櫃處理時間及減少機場地面交通擁塞，同時還能在環保方面減少碳排放。目前台灣機場現況為單車單點人工調度之運送方式，較缺乏系統最佳化的調度策略，因此本研究將考量拖曳盤櫃數量限制以多點運送進行規劃，這種模式將隨著問題規模擴大而大幅減少燃料費用及縮短行車時間。為了能夠解決現今地球暖化日益嚴重的現象，減少溫室氣體以及節省能源消耗已經成為各國至關重要之議題，期盼未來能夠降地機場地勤車輛之能源消耗，以系統化之運送策略減少縮短行車路線。
雖然過去已有學者將最佳化方法應用於航空地勤相關問題，但多著重在人員班表、旅客行李運送或遠端機坪接駁車輛規劃等問題，因此將本研究基於桃園機場地勤業之立場，以地勤車輛拖曳盤櫃至停機坪之最少成本為目標，考量實際運營情況，利用整數數學規劃及時空網路流動技巧建立模式，並利用Python進行程式撰寫配合Gurobi最佳化軟體進行模式求解，最後並針對不同車隊數量、不同車輛及盤櫃停等成本、不同盤櫃數量、不同出車成本進行敏感度分析，結果顯示本研究模式為一有效的輔助工具，期望能改善機場龐大貨運量之下之路徑規劃。

The scheduling of container tugs involves not only route planning but also the constraint on the number of containers each tug can tow, making it significantly different from traditional vehicle routing problems. With increasing demand in airport ground operations post-pandemic, the problem has grown more complex.From a system optimization perspective, computer-based planning can shorten handling times, reduce ground traffic congestion, and lower carbon emissions. Currently, Taiwan airports rely on manual single-tug, single-point dispatching, lacking optimized strategies. This study proposes a multi-point dispatch model that considers container limits per tug, aiming to reduce fuel costs and travel time as the problem size increases.Given global concerns over climate change, energy saving and emission reduction are critical goals. A systematized dispatch strategy is expected to lower energy use for GSE at airports by optimizing travel routes.

While past research has applied optimization to ground operations, it often focuses on staff scheduling, baggage handling, or shuttle planning. This study targets cost minimization for towing containers to aircraft stands at Taoyuan Airport. It incorporates real operational constraints using integer programming and time-space network flow models, used Python programming language and Gurobi optimization software to solve the proposed models, The results show that proposed model is an effective auxiliary tool, which is expected to improve the route planning under the huge cargo volume of airports.

目錄
目錄	I
圖目錄	III
表目錄	V
第一章 緒論	1
1.1研究背景與研究動機	1
1.2研究目的	3
1.3現況分析與研究範圍	3
1.4研究流程	6
第二章 文獻回顧	8
2.1機場地勤車輛指派及調度相關文獻	8
2.2車輛途程問題相關文獻	10
2.3網路流動技巧相關文獻	11
2.4其他路線規劃問題文獻	12
2.5小結	14
第三章 模式構建	15
3.1問題描述	15
3.2模式基本條件或已知資訊	17
3.3模式架構	18
3.3.1拖曳車時空網路	18
3.3.2出口盤櫃時空網路	20
3.3.3進口盤櫃時空網路	22
3.4建構數學定式	24
第四章 實證分析	28
4.1模式測試與結果分析	28
4.1.1輸入資料與輸出資料	28
4.2小規模測試	29
4.3基本範例測試	33
4.3.1參數設定	33
4.3.2基本範例測試	34
4.4敏感度分析	35
4.4.1車輛數之敏感度分析	36
4.4.2盤櫃停等成本之敏感度分析	41
4.4.3出口盤櫃數量之敏感度分析	44
4.4.4進口盤櫃數量之敏感度分析	47
4.4.5出車成本改變之敏感度分析	50
第五章 結論與建議	53
5.1結論	53
5.2建議	55
參考文獻	57

 
圖目錄
圖1.1拖曳車與平車圖	4
圖1.2機場配置圖	6
圖1.3本研究流程圖	7
圖3.1單車單點規劃方式之示意圖	15
圖3.2考量系統最佳化規劃方式之示意圖	16
圖3.3拖曳車時空網路示意圖	20
圖3.4出口盤櫃時空網路示意圖	22
圖3.5進口盤櫃時空網路示意圖	24
圖4.1小規模範例之進出口盤櫃裝卸資料	30
圖4.2小規模範例之車輛移動資料	30
圖4.3小規模範例之路徑圖1	31
圖4.4小規模範例之路徑圖2	32
圖4.5車輛數改變之敏感度分析趨勢圖	38
圖4.6盤櫃停留成本之敏感度分析變化趨勢圖	42
圖4.7盤櫃停留成本對進出口盤櫃之敏感度分析趨勢圖	42
圖4.8出口盤櫃數量之模式規模變化趨勢圖	45
圖4.9出口盤櫃數量之敏感度分析結果趨勢圖	45
圖4.10進口盤櫃數量模式規模趨勢圖	48
圖4.11進口盤櫃數量之敏感度分析結果變化趨勢圖	48
圖4.12出車成本之敏感度分析結果變化趨勢圖	52

 
表目錄
表4-1小樣本範例測試之模式規模與求解結果	35
表4-2車輛數之敏感度分析	39
表4-3盤櫃停等成本之敏感度分析	43
表4-4出口盤櫃數量之敏感度分析	46
表4-5進口盤櫃數量之敏感度分析	49
表4-6出車成本之敏感度分析	51

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