字體大小: 字級放大   字級縮小   預設字形  

詳目顯示

以作者查詢圖書館館藏以作者&題名查詢臺灣博碩士以作者查詢全國書目
研究生中文姓名:謝秉渲
研究生英文姓名:Hsieh, Ping-hsuan
中文論文名稱:隨機需求下航空公司裝載設備即時調度規劃
英文論文名稱:Real-time dispatching for airline unit load device under stochastic demands
指導教授姓名:湯慶輝
口試委員中文姓名:教授︰盧宗成
教授︰林振榮
學位類別:碩士
校院名稱:國立臺灣海洋大學
系所名稱:運輸科學系
學號:10668017
請選擇論文為:學術型
畢業年度:108
畢業學年度:107
學期:
語文別:中文
論文頁數:77
中文關鍵詞:裝載設備即時階段隨機需求確定需求即時調整情境樹模擬
英文關鍵字:Unit load deviceReal-time adjustmentScenario treeStochastic demandDeterministic demandSimulated daysProbability combination
相關次數:
  • 推薦推薦:0
  • 點閱點閱:32
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:3
  • 收藏收藏:0
航空公司裝載設備(Unit load device, ULD),即一般所稱的貨盤或貨櫃,為航空公司每日營運不可或缺的裝載用具。在現實營運中通常會發生所需的ULD產生隨機的變化。其中,客機ULD的隨機需求源自於旅客行李數量的變化,影響裝載旅客行李所需的ULD數量,同時亦影響客機機腹多餘艙位之數量,進而造成多餘艙位可裝載其他ULD的數量。而貨機則因原規畫的貨物量在即時階段可能產生變化,進而造成所需的ULD產生隨機的變化。因此航空公司每日在即時階段皆會進行ULD之臨時調度與成本管理,以有效率使用ULD,避免場站ULD短缺或閒置的情形,同時亦減少因ULD臨時調度所衍生增加的營運成本。然而,目前針對ULD在即時階段進行調度之相關文獻較為缺乏,故本研究考量業者在即時階段可能採用的4項不同的調度方式,透過系統化的方式幫助航空公司有效率地在即時階段進行ULD調度規劃與降低ULD臨時調度增加的營運成本,提升其營運績效。
本研究利用數學規劃方法,首先配合即時階段4項調度方式,構建一ULD即時調度時空網路問題,以定式4項調度方式的ULD流動情形。另外,設計一即時調整架構,將一天依時間順序期劃分為不同階段。由於機場在實際運營時,每日各階段航班數皆會不同,因此本研究依照各階段之航班數與求解時間,即時將原時程劃分為不同階段之決策期長短,以確保各階段能符合在即時階段求解時間上的限制。此外,在各階段運用情境樹的概念表達各航班需求的狀況,其中,各情境樹中包含ULD確定需求之航班與隨機需求之航班,ULD確定需求之航班僅有一個確定之ULD需求,ULD隨機需求之航班則包含多種不同之隨機需求狀況。此外,本研究亦針對ULD隨機需求航班的不同之隨機需求狀況,設計高端需求、一般需求、平均需求與低端需求下之四種不同的機率值組合,以了解此不同機率組合下之結果。最後,我們以國內一航空公司資料為例進行測試分析,並模擬50日、100日與500日之結果,並根據研究的結果,提出結論與建議。
The unit load device (ULD) is essential for an airline’s daily operations. The ULD needs to be dispatched among an airline’s operating stations to avoid ULD shortage and idleness. However, stochastic passenger baggage and cargo demands are usually occurred, making the dispatch of ULD needed to be adjusted in real-time operations. At present, there is a lack of relevant literature on ULD dispatching in the real-time stage. Therefore, this study considers four different dispatching methods and develops a real-time adjustment ULD dispatching model in the real-time stage, which is expected to help airlines efficiently perform ULD dispatching in the real-time stage.
simulated We use the mathematical programming method combined with the four different dispatching methods in the real-time stage, combined with the real-time adjustment time-space network technique to formulate the problem. We use the scenario tree concept to divide the ULD demands into different situations, and construct an real-time stage ULD adjustment dispatching model. In this study, the original decision-making period is divided into different stages by real-time adjustment method to meet the limitation of the real-time stage solution. We consider the flight departure time, divide the demand into the determined and the stochastic demand, and use the scenario tree to divide the flight demand into high demand, medium demand and low demand. In order to test the difference between this research model and the actual results, and analyzed 50th, 100th, and 500th days results. In addition, to understand the results of the different probability combinations of the three random demand conditions. Therefore, four different probability combinations are designed, which are the highest demand, general demand, average demand and lowest demand, to understand the results under different probability combinations. Finally, we perform a case study using the real operating data from a Taiwan airline. The test results show the good performance of the model and solution algorithm.
誌 謝 I
摘 要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 2
1.4研究流程 3
第二章 文獻回顧 4
2.1 航空公司ULD短期調度與規模 4
2.2 即時階段之相關文獻 5
2.3 隨機規劃之相關文獻 6
2.3.1 兩階段隨機規劃相關文獻 6
2.3.2 多階段隨機規劃文獻 7
2.4 本章小結 8
第三章 問題描述 9
3.1 問題描述 9
第四章 求解流程與即時調度模式 16
4.1 各階段情境求解設計 16
4.2 ULD即時調度時空網路設計 18
4.2.1 自有ULD即時調度時空網路 18
4.2.2 他航ULD即時調度時空網路 20
4.3即時階段調度與網路設計方式 23
4.4 模式定式 29
第五章 實證案例測試 33
5.1 資料分析 33
5.1.1 航班資訊 33
5.1.2 各機型載運ULD相關資料 33
5.1.3 各型ULD裝拆打盤作業時間 35
5.1.4 成本資料 35
5.1.5 隨機需求機率值 36
5.1.6 即時調整參數設定 36
5.2 即時調整設計 37
5.3 測試結果與分析 38
5.3.1 模擬50日 38
5.3.2 模擬100日 41
5.3.3 模擬500日 44
5.3.4 綜合比較 47
5.4各機率組合調度成本差異分析 49
5.4.1 機率組合 – 高端需求 49
5.4.2 機率組合 – 一般需求 50
5.4.3 機率組合 – 平均需求 53
5.4.4 機率組合 – 低端需求 56
5.4.5 各機率組合調度成本差異分析小結 58
第六章 結論與建議 59
6.1 結論 59
6.2 建議 60
參考文獻 61
附錄一:模擬50日 64
附錄二:模擬100日 65
附錄三:模擬500日 67

1. 王蕙萱,「供應鏈多階段隨機規劃暨動態定價之研究」,碩士論文,國立成功大學交通管理科學系,2009。
2. 林孟嫻,「航空公司最適裝載設備調度與規模之研究」,碩士論文,國立交通大學運輸與物流管理學系,2017。
3. 張芯瑋,「以基因演算法求解隨機需求下航空快遞貨物裝櫃規劃問題之研究」,碩士論文,國立嘉義大學運輸與物流工程研究所,2008。
4. 張益菁,「考量需求不確定之單階多廠產能規劃問題—以 TFT-LCD 產業為例」,碩士論文,國立清華大學工業工程與工程管理學系,2007。
5. 楊大輝、李綺容,「需求變動下之航空貨運網路規劃」,運輸學刊, 第十九卷,第二期,頁169-189, 2007。
6. 盧華安、陳倩怡、卓建宏、陳明宏,「國際航空貨運盤櫃設備最適規模及調度限制之研究」,運輸學刊,第二十一卷,第四期,頁385-412,2009。
7. 錢韋宏,「基因演算法求解凹形成本函數之隨機軸輻式網路」,碩士論文,國立高雄第一科技大學運籌管理系,2010。
8. 蕭妃晏,「隨機需求下航空快遞貨物裝櫃規劃模式之研究」,碩士論文,國立中央大學土木工程研究所,2005。
9. 顏禎毅,「變動需求下航空公司裝載設備調度與規模之研究」,碩士論文,國立臺灣海洋大學運輸科學學系,2016。
10. Adibi, A., & Razmi, J. (2015), “2-Stage stochastic programming approach for hub location problem under uncertainty: A case study of air network of Iran, ” Journal of Air Transport Management, 47, pp.172-178.
11. Agra, A., Christiansen, M., Delgado, A., & Hvattum, L. M. (2015). “A maritime inventory routing problem with stochastic sailing and port times.” Computers & Operations Research, 61, pp. 18-30.
12. Bennell, J. A., Mesgarpour, M., & Potts, C. N. (2017). “Dynamic scheduling of aircraft landings.” European Journal of Operational Research, 258(1), pp. 315-327.
13. Cardoso, S. R., Barbosa-Póvoa, A. P., Relvas, S., & Novais, A. Q. (2015). “Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty.” Omega, 56, pp. 53-73.
14. Chen, D., Hu, M., Zhang, H., Yin, J., & Han, K. (2017). “A network based dynamic air traffic flow model for en-route airspace system traffic flow optimization.” Transportation Research Part E: Logistics and Transportation Review, 106, pp. 1-19.
15. Christian, B., & Cremaschi, S. (2018). “A multistage stochastic programming formulation to evaluate feedstock/process development for the chemical process industry.” Chemical Engineering Science, 187, pp. 223-244.
16. Ding, X., Hua, D., Jiang, G., Bao, Z., & Yu, L. (2017). “Two-stage interval stochastic chance-constrained robust programming and its application in flood management.” Journal of Cleaner Production, 167, pp. 908-918.
17. Dowson, O., Philpott, A., Mason, A., & Downward, A. (2019). “A multi-stage stochastic optimization model of a pastoral dairy farm.” European Journal of Operational Research, 274(3), pp. 1077-1089.
18. Errico, F., Desaulniers, G., Gendreau, M., Rei, W., & Rousseau, L.-M. (2016). “A priori optimization with recourse for the vehicle routing problem with hard time windows and stochastic service times.” European Journal of Operational Research, 249(1), pp. 55-66.
19. Elçi, Ö. & Noyan, N. (2018). “A chance-constrained two-stage stochastic programming model for humanitarian relief network design.” Transportation research part B: methodological, 108, pp. 55-83.
20. Fattahi, M., & Govindan, K. (2018). “A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: A real-life case study.” Transportation Research Part E: Logistics and Transportation Review, 118, pp. 534-567.
21. Garrido, R. A., Lamas, P., & Pino, F. J. (2015). “A stochastic programming approach for floods emergency logistics.” Transportation Research Part E: Logistics and Transportation Review, 75, pp. 18-31.
22. Hafiz, F., de Queiroz, A. R., Fajri, P., & Husain, I. (2019). “Energy management and optimal storage sizing for a shared community: A multi-stage stochastic programming approach.” Applied energy, 236, pp. 42-54.
23. Hasany, R. M., & Shafahi, Y. (2017). “Two-stage stochastic programming for the railroad blocking problem with uncertain demand and supply resources.” Computers & Industrial Engineering, 106, pp. 275-286.
24. Homem-de-Mello, T., & Pagnoncelli, B. K. (2016). “Risk aversion in multistage stochastic programming: A modeling and algorithmic perspective.” European Journal of Operational Research, 249(1), pp. 188-199.
25. Hosseini, M., & MirHassani, S. (2015). “Refueling-station location problem under uncertainty.” Transportation Research Part E: Logistics and Transportation Review, 84, pp. 101-116.
26. Hsu, P.-Y., Angeloudis, P., & Aurisicchio, M. (2018). “Optimal logistics planning for modular construction using two-stage stochastic programming.” Automation in Construction, 94, pp. 47-61.
27. Hu, S., Han, C., Dong, Z. S., & Meng, L. (2019). “A multi-stage stochastic programming model for relief distribution considering the state of road network.” Transportation Research Part B: Methodological, 123, pp. 64-87.
28. Karabuk, S., & Manzour, H. (2019). “A multi-stage stochastic program for evacuation management under tornado track uncertainty.” Transportation research part E: logistics and transportation review, 124, pp. 128-151.
29. Li, C., Cai, Y., & Qian, J. (2018). “A multi-stage fuzzy stochastic programming method for water resources management with the consideration of ecological water demand.” Ecological indicators, 95, pp. 930-938.
30. Lu, H. A., & Chen, C. Y. (2011), “A time–space network model for unit load device stock planning in international airline services, ”Journal of Air Transport Management,17,2, pp.94-100.
31. Lu, H. A., & Chen, C. Y. (2012), “Safety stock estimation of unit load devices for international airline operations,” Journal of Marine Science and Technology, 20,4, pp.431-440.
32. Maggioni, F., Cagnolari, M., Bertazzi, L., & Wallace, S. W. (2019). “Stochastic optimization models for a bike-sharing problem with transshipment.” European Journal of Operational Research, 276(1), pp. 272-283.
33. Mavromatidis, G., Orehounig, K., & Carmeliet, J. (2018). “Design of distributed energy systems under uncertainty: A two-stage stochastic programming approach.” Applied Energy, 222, pp. 932-950.
34. Repko, M. G., & Santos, B. F. (2017). “Scenario tree airline fleet planning for demand uncertainty.” Journal of Air Transport Management, 65, pp. 198-208.
35. Şafak, Ö. Çavuş, Ö., & Aktürk, M. S. (2018). “Multi-stage airline scheduling problem with stochastic passenger demand and non-cruise times.” Transportation Research Part B: Methodological, 114, pp. 39-67.
36. Song, D.-P., Li, D., & Drake, P. (2015). “Multi-objective optimization for planning liner shipping service with uncertain port times.” Transportation Research Part E: Logistics and Transportation Review, 84, pp. 1-22.
37. Yu, S. P., Cao, X. B., & Zhang, J. (2011). “A real-time schedule method for Aircraft Landing Scheduling problem based on Cellular Automation.” Applied Soft Computing, 11(4), pp. 3485-3493.
38. Zhang, C., & Guo, P. (2018). “An inexact CVaR two-stage mixed-integer linear programming approach for agricultural water management under uncertainty considering ecological water requirement.” Ecological Indicators, 92, pp. 342-353.
39. Zhang, D., & Klabjan, D. (2017). “Optimization for gate re-assignment.” Transportation Research Part B: Methodological, 95, pp. 260-284.
40. Zhang, S., & Cardin, M. A. (2017). “Flexibility and real options analysis in emergency medical services systems using decision rules and multi-stage stochastic programming.” Transportation research part E: logistics and transportation review, 107, pp. 120-140.
41. Zhang, X., Guan, X., Zhu, Y., & Lei, J. (2015). “Strategic flight assignment approach based on multi-objective parallel evolution algorithm with dynamic migration interval”. Chinese Journal of Aeronautics, 28(2), pp. 556-563.
42. Zhang, Z., Hao, Z., & Gao, Z. (2015). “A dynamic adjustment and distribution method of air traffic flow en-route”. Journal of Air Transport Management, 42, pp.15-20.
(此全文20240727後開放外部瀏覽)
電子全文
全文檔開放日期:2024/07/27
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
* *