臺灣博碩士論文加值系統

貨運機隊排程與班次表規劃對航空業者的貨運營運績效而言，甚為重要。其結果不但攸關航空業者設備之使用效率、左右班次表之擬訂與人員之調度，更重要的，將進而直接影響及業者之獲利能力、服務水準與市場之競爭能力等。然而，目前的航空公司對於航空貨運的排班與網路設計，大多利用人工以經驗法則進行，不僅廢時且難以得到最佳班次表，而降低營運績效。以往一般飛航排程研究多以客運為主，但航空客運與貨運在本質上並不相同。在客運方面，在航點的選擇上多屬於中長期之規劃；然在貨運部分，業者可就貨物之實際需求與經濟利益的考量下，在短期的排班上，選擇服務的航點，故在航點之選擇上可納入短期之規劃。另外，客運的旅次需求之時間敏感度高，若轉運次數過多，易使旅客流失。然而，對貨運而言，由於旅次需求之時間敏感度相對較低，故較不在乎被轉運多次。此外，對同一起迄對的貨運需求，各貨物的時間敏感度可能不完全相同，運送上可依各貨物之時效性，綜合機隊排程的系統考量，採取最有效的運送。
本研究以航空業者立場，在給定的營運資料下，包括機隊規模、起降額度、可用時間帶、航站貨物處理成本、飛航成本等，以營運利潤最大化為目標，考量貨物運送的相關營運限制，針對航點選擇、機隊規劃與班次表規劃等三大重要作業，建構一短期貨運之航點選擇、機隊排程及班次表建立的整合規劃模式。本研究模式能於實務的應用上，提供一有效的工具，以輔助航空貨運業者在短期營運中規劃合適的航點、排程與班次表。本研究利用網路流動技巧建立模式，此模式主要包含多重物流時空網路與機流時空網路，用以定式貨物與機隊在時空中的流動。其中在物流時空網路將有別於傳統之飛航網路設計，係依據不同時效性之貨物需求，構建多重起迄時對(OD-time pair)時空網路。機流時空網路則以整數流動方式定式機隊的週期排程。在物流時空網路與機流時空網路中及其間，再加上實務的營運限制，以符合實際的飛航作業。此模式可定式為一混合整數多重網路流動問題，屬NP-hard問題。本研究利用數學規劃軟體，自行發展有效的策略解與啟發解法進行求解。最後本研究以一國籍航空公司之國際貨運航線營運資料為例，進行範例測試與分析，進而提出結論及建議。

Cargo Fleet routing and flight scheduling are essential to airline cargo operations. In particular, they always affect the usage efficiency of facilities, the establishment of timetables and the crew scheduling. As a result, they are essential to carriers’ profitability, level of service and competitive capability in the market. However, most of the airlines in Taiwan currently adopt a trial-and-error process for cargo fleet routing and flight scheduling practices. Such an approach is considered to be less efficient when the flight network become larger, and can possibly result in an inferior feasible solution. In the past most research on airline scheduling was mainly focused on passenger transportation, which is fundamentally different from air cargo transportation. In particular, airport selecting in service network design is typically in the stage of long-term plan in passenger transportation, but in cargo transportation, due to possibly significant demand changes in short-term operations, carriers may perform their airport selecting, fleet routing and timetable setting together in the stage of short-term plan, according to considerations of demand and profit. Moreover, passengers are more sensitive to time than cargos. Too many transfers in a transport service may result in significant loss of passengers, but not much loss of cargos. Besides, cargos with the same OD may be sensitive to different times, which can be incorporated into fleet routing systematically in order to find the most effective transport plan.
In this research, given the operating data, including fleet size, airport flight quota and available time slots, cargo handling cost at airports and flight cost, on the basis of the carrier’s perspective, we develop an integrated scheduling model by combining airport selecting, fleet routing and timetable setting, with the objective of maximizing the operating profit, subject to the related operating constraints. The model is a useful planning tool for cargo airlines to determine suitable service airports, fleet routes and timetables in their short-term operations. We employ network flow techniques to construct the model, which include multiple cargo- and fleet-flow networks in order to formulate the flows of cargos and fleet in the dimensions of time and space. In the cargo-flow networks, different from that in the past research, we construct multiple OD-time-pair time-space networks on the base of cargos’ timeliness. In the fleet-flow networks, we use an integer flow network to formulate the periodical fleet routes. Some side constraints set between the cargo- and fleet-flow networks according to the real operating requirements. The model formulated as a mixed integer program that is characterized as an NP-hard problem. We employ a mathematical programming solver and develop a heuristic to solve the problem. Finally, to evaluate the model and the solution algorithm, we perform a case study using real cargo operating data from a major Taiwan airline.

第一章 緒論1
1.1研究背景與動機1
1.2研究目的與範圍3
1.3研究方法與流程3
第二章 文獻回顧5
2.1飛航網路設計與頻次規劃相關文獻5
2.2短程飛航排程相關文獻7
2.3小結10
第三章 模式構建11
3.1模式假設11
3.2模式架構12
3.2.1網路設計考量因素12
3.2.2機流時空網路14
3.2.3物流時空網路17
3.2.4排程營運限制19
3.3數學定式20
3.3.1符號說明20
3.3.2數學定式21
3.4模式延伸22
3.5小結23
第四章 求解方法24
4.1混合策略解法24
4.2啟發解法26
4.3單機定線29
4.4小結29
第五章 範例測試30
5.1資料輸入30
5.1.1航線資料30
5.1.2機場起降時間帶及額度限制31
5.1.3航機種類及機隊規模33
5.1.4貨物起迄時對資料34
5.1.5成本資料34
5.1.6運價資料36
5.2輸出資料37
5.2.1 策略解結果輸出37
5.2.2 啟發解結果輸出39
5.2.3 航段額度結果輸出42
5.3敏感度分析45
5.3.1機隊規模敏感度分析45
5.3.2起迄時對貨運需求量敏感度分析48
5.3.3航站固定成本敏感度分析51
5.3.4單一航站貨運需求敏感度分析53
5.3.5航段額度為輸入值敏改度分析56
5.4 小結58
第六章 結論與建議59
6.1 結論59
6.2 建議60
6.3 貢獻61
參考文獻63
附錄66
附錄一 CPLEX callable library code66
附錄二 啟發解求解結果與敏感度分析結果67

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