臺灣人文及社會科學引文索引資料庫系統

:::

詳目顯示

回上一頁
第 1 筆 / 總合 1 筆   第一頁 上一頁 下一頁 最後一頁
/1頁
題名：近洋貨櫃航商艙位配置及空櫃調度之最適化研究
作者：張嘉惠 引用關係
作者(外文)：Chang, Chia-Hui
校院名稱：國立交通大學
系所名稱：交通運輸研究所
指導教授：馮正民
學位類別：博士
出版日期：2009
主題關鍵詞：收益管理；艙位配置；空櫃配送；貨櫃航商；Revenue Management；Slot Allocation；Empty Container Reposition；Container Carrier
原始連結：連回原系統網址new window
相關次數：
  • 被引用次數被引用次數:期刊(1) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:1
  • 共同引用共同引用:0
  • 點閱點閱:0
貨櫃航商主要經營海上定期航線，藉由運送貨物以賺取運費收入，其航線泊靠港口遍佈世界各地，在貨物承攬部份主要與各地之貨務運輸代理商合作，透過其承攬貨物。實務上，貨櫃航商通常重視“船舶滿載”，忽略貨物流向及運費收入管理，而貨務運輸代理商收入主要來自運費中之佣金，因此他們通常會爭取多裝載貨物以增加佣金收入，在市場繁榮時，各地之貨務運輸代理商常常會因艙位問題而引起衝突，最常發生情形為在某一航線上，先泊靠港口之代理商會超裝貨物在貨櫃船上，導致後續泊靠港口之代理商面臨船上無艙位可裝載之情況。在國際貿易中，貨物通常從出口導向國家運送至進口導向國家，此一貨物流向不平衡現象為全球性且無法避免，貨櫃航商因此面臨部份港口累積大量空櫃(積櫃港)，部份港口卻面臨缺乏空櫃以裝載客戶待運送之貨物(缺櫃口)，因此貨櫃航商須負擔大量之空櫃調度費用，在實務之空櫃調度，貨櫃航商通常安排從積櫃港一次調度大量的空櫃或將空櫃運送至較遠地區之缺櫃港，而這些空櫃將佔用貨櫃船上的艙位，其結果將造成貨櫃船上減少運送重櫃而賺取運費收入之機會；貨櫃航運業是一個競爭性的服務業，為了增加公司的競爭優勢，貨櫃航商須對其運費收入及支出費用進行謹慎管理與控制。
過去國內外有部份學者提出艙位配置及空櫃管理的相關研究，在艙位配置相關研究中缺乏以近洋航線多港口泊靠及納入空櫃調度成本之研究，在空櫃管理中缺乏以貨櫃航商經營之海上運輸網絡進行公司整體空櫃調度之研究；因此本研究以亞洲區間之貨櫃航商為研究標的，近洋航線主要特性為：航線航程較短、泊靠港口較多且每個港口裝載及卸載頻繁，並分為艙位配置及空櫃管理二部份加以探討。第一部份，本研究提議以收益管理之概念建立重櫃艙位配置計畫，並將空櫃調度之期望成本納入目標式，以反應貨物流向不平衡之成本，其模式透過線性規劃求得貨櫃航商在單一航線上之單一航次利潤最大化，其限制條件包含船舶艙位容量、船舶重量及各港口之貨櫃需求。本模式應用國內某航商在一近洋航線為案例進行應用與分析，其模式結果與實際航行裝載情形比較，本模式之結果不僅可獲得較佳收益，並可作為貨櫃航商在管理各地貨務運輸代理商之指導方針，以避免因艙位問題而引起貨物運輸代理商間之衝突。
第二部份，本研究提出將貨櫃航商經營之海上運輸網絡分為數個地理區域，空櫃配送則在單一地理區域內進行，此舉可避免實務上因長途運送空櫃而佔用貨櫃船上之艙位；在模式建構上分為上、下二層問題，上層問題先考量各個港口在某一段間內貨櫃移動情形，包括進口貨櫃、出口貨櫃、空櫃搬入及空櫃搬出等因素，以區分各港口之特性(積櫃港/缺櫃港)及數量(供應量/需求量)；下層問題則考量到以不同運送模式(自有艙位/租賃艙位/內陸拖運)之成本差異建構最佳空櫃配送計畫，其模式透過線性規劃之運輸問題求算總體空櫃運送成本最小，將積櫃港之空櫃運用至缺櫃港，本模式應用國內某航商整體貨櫃流向資料為案例進行應用與分析，模式結果可做為貨櫃管理部門在安排空櫃調度之參考，並建議將其空櫃安排分數個航次裝載至航線剩餘之艙位(空艙位)，並進行進一步之分析，可獲得各港口之空櫃調度所面臨之問題，短期可透過改變空櫃安全存量、租用艙位、額外派遣船泊或暫時改變其他航線航程來解決部份港口空櫃調度不易之情形，長期則可考量調整公司整體海上運輸網絡。
Container carriers gain freight revenue by delivering containers from one port to another and depend on shipping agencies to provide cargo. Since a fully loaded carrier brings immediate revenue that is higher than that of a partially loaded carrier, cargo flow and freight revenue management are often ignored. To improve their own revenue, which is supplemented by commissions from ocean freight, shipping agencies typically compete for additional slots on containerships. In booming markets, arguments over slot allocation between shipping agencies occur frequently. These disputes, when coupled with the mismanagement of freight revenue on the part of containerships, often result in a loss of revenue for both shipping agencies and carriers. Container carriers tend to accumulate a large number of unnecessary empty containers at particular ports while other ports face a shortage of empty containers. In practice, carriers often reposition a considerable number of empty containers to others ports with shortage, during a single voyage. However, the operational expenses are substantial when an accumulation of this sort occurs. Empty containers also occupy slots on containerships with the result that carriers are unable to take aboard loaded containers yielding freight revenue. In order to increase their competitive edge, container carriers need to manage revenue and control expenditures.
Several studies have been conducted on slot allocation and empty container management. A few of these studies have sought to maximize profits on short-sea, multiple-port service routes by considering the cost of empty container repositioning. Little attention has been paid to the management of such repositioning within the sea transportation network. This study, which focuses on short-sea service intra-Asian routes, focuses on both aspects of repositioning. The main characteristics of intra-Asian service routes include: voyage distance is short, there are multiple-port calls, and loading and unloading is frequent at each port. These observations are factored into this study which is divided into two parts. The first part incorporates the concept of revenue management with expected cost of empty container repositioning, by offsetting cargo imbalance. Here an optimal model has been formulated via linear programming to maximize operational profit, subject to the constraints of vessel capacity, vessel deadweight, and container demand. A Taiwan container carrier has been used as a case study. The analytical results show that by implementing the proposed model, containerships can increase profits and shipping agencies might avoid friction in a booming market.
The second part of this study proposes to partition the sea transportation network into several geographical regions and distribute empty containers within a single region, in order to reduce the number of occupied slots over a long distance. There are two challenges to this proposal. The first challenge, which is termed the “upper-problem,” lies in identifying and estimating empty container stock for each port. The second challenge or “lower-problem” concerns incorporating modes of transportation into the model. The empty container reposition model that is deemed optimal has been formulated via linear programming with a view to overcoming the transportation problem and minimizing the total cost of transferences within a single region. Here again, the research uses data obtained from a Taiwan container carrier. When this data is applied for analysis, the results show that the allocation of empty containers can be optimized by repositioning them over the course of several voyages where they can occupy unsold slots. With regard to port characteristics, this study proposes the following strategies to solve empty container problems: charter slots, launching a containership for extra service, or introducing a temporary change in the service route. These are all short-term solutions. In the long-term, sea ports might need to restructure their sea transportation network.
1. Badinelli, R.D., 2000. An optimal, dynamic policy for hotel yield management. European Journal of Operational Research 121, 476-503.
2. Belobaba, P.P., Wilson, J.L., 1997. Impacts of yield management in competitive airline markets. Journal of Air Transport Management 39(1), 3-9.
3. Bergation, A.S., Veenstra, A.W., 2002. Interconnection and co-ordination: an application of network theory to liner shipping. International Journal of Maritime Economics 4, 231-248.
4. Berman, B., 2005. Applying yield management pricing to your service business. Business Horizon 48, 169-179.
5. Botimer, T.C., 1996. Efficiency considerations in airline pricing and yield management. Transportation Research Part A 30(4), 307-317.
6. Chang, H., Jula, H., Chassiakos, A., Ioannou, P., 2008. A heuristic solution for the empty container substitution problem. Transportation Research Part E 44, 203-216.
7. Chen, C.Y., Chiu, M.C., 2002. A network design model for the containership routing problem. Transportation Planning Journal 32(2), 267-298. (in Chinese)
8. Choong, S.T., Cole, M.H., Kutanoglu, E., 2002. Empty container management for intermodal transportation networks. Transportation Research Part E 38, 423-438.
9. Chou, C.C., 2006. A model for solving the empty-container allocation problem. Commerce and Management Quarterly 7(1), 59-84. (in Chinese)


10. Chou, C.C., 2007. A fuzzy MCDM method for solving marine transshipment container port selection problems. Applied Mathematics and Computation 186, 435-444.
11. Cullinane, K., Khanna, M., 1999. Economies of scale in large container ship. Journal of Transport Economics and Policy 33, 185-208.
12. Cullinane, K., Khanna, M., 2000. Economies of scale in large containership: optimal size and geographical implications 8, 181-195.
13. Ding, J.F., Liang, G.S., 2005. Using fuzzy MCDM to select partners of strategic alliances for liner shipping. Information Science 173, 197-225.
14. Fagerholt, K., 1999. Optimal fleet design in a ship routing problem. International Transactions in Operational Research 6, 453-464.
15. Fagerholt, K., Christiansen, M., 2000. A combined ship scheduling and allocation problem. Journal of the operational research society 51, 834-842.
16. Fagerholt, K., 2004. Designing optimal routes in a liner shipping problem. Maritime Police & Management 31(4), 259-268.
17. Fremont, A., 2007. Global maritime networks: The case of Maersk. Journal of Transport Geography 15, 431-442.
18. Gorin, T., Belobaba, P., 2004. Impacts of entry in airline markets: effects of revenue management on traditional measures of airline performance. Journal of Air Transport Management 10, 259-270.
19. Harris, F.H.D., Pinder, J.P., 1995. A revenue management approach to demand management and order booking in assemble-to-order manufacturing. Journal of Operations Management 3, 299-309.
20. Hsieh, S.H., Chang, F.R., 2001. Applications of the hub-and-spoke network model in routing liner ships. Transportation Planning Journal 30(4), 871-890. (in Chinese)
21. Hsu, C.I., Hsieh, Y.P., 2007. Routing, ship size, and sailing frequency decision-making for a maritime hub-and-spoke container network. Mathematical and Computer Modelling 45, 899-916.
22. Inderfurth, K., Minner, S., 1998. Safety stocks in multi-stage inventory systems under different service measures. European Journal of Operational Research 106, 57-73.
23. Jula, H., Chassiakos, A., Ioannou, P., 2006. Port dynamic empty container reuse. Transportation Research Part E 42, 43-60.
24. Lai, K.K., Ng, W.L., A stochastic approach to hotel revenue optimization. Computers & Operations Research 32, 1059-1072.
25. Lai, M.F., Lo, H.K., 2004. Ferry service network design: optimal fleet size, routing, and scheduling. Transportation Research Part A 38, 305-328.
26. Lam, S.W., Lee, L.H., Tang, L.C., 2007. An approximate dynamic programming approach for the empty container allocation problem. Transportation Research Part C 15, 265-277.
27. Li, J.A., Liu, K., Leung, S.C.H., Lai, K.K., 2004. Empty container management in a port with long-run average criterion. Mathematical and Computer Modelling 40, 85-100.
28. Li, J.A., Leung, S.C.H., Wu, Y., Liu, K., 2007. Allocation of empty containers between multi-ports. European Journal of Operational Research 182, 400-412.
29. Lu, H.A., Hsu, Y.C., 2001. Route selection and fleet deployment for a container liner. Transportation Planning Journal 30(3), 577-602. (in Chinese)
30. Lu, H.A., 2002. Routing planning for container liner. Transportation Planning Journal 31(1), 121-142. (in Chinese)
31. Lu, H.A., 2003. Modeling ship’s routing and container positioning for transoceanic liner. Transportation Planning Journal 32(3), 423-446. (in Chinese)
32. Maia, L.O.A.M., Qassim, R.Y., 1999. Minimum cost safety stocks for frequent delivery manufacturing. International Journal of Production Economics 62, 233-236.
33. Midoro R., Pitto, A., 2000. A critical evaluation of strategic alliances in liner shipping. Maritime Policy and Management 27(1), 31-40.
34. Minner, S., 2001. Strategic safety stocks in reverse logistics supply chains. International Journal of Production Economics 71, 417-428.
35. Norman, E.D., Mayer, K.J., 1997. Yield management in Las Vegas casino hotels. Hotel and Restaurant Administration Quarterly, October 1997, 29-33.
36. Ryoo, D.K., Thanopoulou, H.A., 1999. Liner alliances in the globalization era: a strategic tool for Asian container carriers. Maritime Police and Management 26(4), 349-367.
37. Shen, W.S., Khoong, C.M., 1995. A DSS for empty container distribution planning. Decision Support Systems 15, 75-82.
38. Shintani, K., Imai, A., Nishimura, E., Papadimitriou, S., 2007. The container shipping network design problem with empty container repositioning. Transportation Research Part E 43, 39-59.
39. Shyr, F.Y., Chen, C.M.H., Hwu, C.F., 2003. An evaluation of various strategic alliances among container carriers-a cooperative game approach. Transportation Planning Journal 32(3), 391-422. (in Chinese)
40. Slack, B., Comtois, C., McCalla, R., 2002. Strategic alliances in the container shipping industry: a global perspective. Maritime Policy and Management 2991), 65-76.
41. Song, D.W., Panayides, P.M., 2002. A conceptual application of cooperative game theory to liner shipping strategic alliances. Maritime Policy and Management 29(3), 285-301.
42. Song, D., Zhang, J., Carter, J., Field, T., Marshall, J., Polk, J., Schumacher, K., Proshun, S.R., Woods, J., 2005. On cost-efficiency of the global container shipping network. Maritime Policy and Management 32(1), 15-30.
43. Ting, S.C., Tzeng, G.H., 2002. Liner shipping revenue management: Optimal slot allocation model, Traffic and Transportation 20, 53-78.
44. Ting, S.C., Tzeng, G..H., 2002. Containership slot allocation using fuzzy multi-objective programming. Traffic and Transportation 21, 39-56.
45. Ting, S.C Tzeng, G..H., An optimal containership slot allocation for liner shipping revenue management. Maritime Policy and Management 31(3), 199-211.
46. Ting, S.C., Tzeng, G.H., 2003. Ship scheduling and cost analysis for route planning in liner shipping. Maritime Economics and Logistics 5, 378-392.
47. Vaughan, T.S., 2006. Lot size effects on process lead time, lead time demand, and safety stock. International Journal of Production Economics 100, 1-9.
48. Yan, S., Tu, Y.P., Chang, K.C., 2005. Optimization of container ship stowage plans with variable demand. Transportation Planning Journal 34(3), 335~390.
49. Zhang, C., Liu, J., Wan, Y.W., Murty, K.G., Linn, J.R., 2003. Storage space allocation in container terminals. Transportation Research Part B 37, 883-903. (in Chinese)
50. DREWRY SHIPPING CONSULTANTS, 2005. The Drewry Annual Container Market Review and Forecast 2005/06. (London: Drewry Shipping Consultants Ltd)
51. DREWRY SHIPPING CONSULTANTS, 2006. Container Shipping Insight Quarterly Analysis of the Container Market 1Q06. (London: Drewry Shipping Consultants Ltd)

52. DREWRY SHIPPING CONSULTANTS, 2006. Container Annual 2006/07.
53. DREWRY SHIPPING CONSULTANTS, 2008. Intra-Asia Container Trades: Demystifying the Market.
54. United Nation, 2008, United Nations Conference on Trade and Development Review of Maritime Transport.
 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
:::
無相關博士論文
 
無相關書籍
 
無相關著作
 
無相關點閱
 
QR Code
QRCODE

臺灣人文及社會科學引文索引資料庫

目前上線人數：620（臺灣：620，外國：0） ／ 本年度總使用人次：2625314 ／ 訪客人次（自102年09月）：68422944
國家圖書館著作權聲明 Copyright © 2013 All rights reserved.
本館地址: 100201臺北市中山南路20號． 本館地圖及交通資訊．
總機：(02)23619132．最佳瀏覽解析度為1024x768以上