本研究探討考慮取消預約、放棄預約以及超額預訂的航空及旅館之營收管理問題。此隨機問題為如何將有限的產能分配於服從非同質性卜瓦松過程(non-homogeneous Poisson process)的需求，對於航空業，我們考慮了多重票價等級，旅館業，考慮多重房型、多重房價以及不同入住天數的環境，決策者傾向於拒絕當下價位較低的旅客以接受未來願意出較高價位的顧客，但是也有可能造成最後資源未被使用的情形。 除此之外，已接受的顧客可能在被提供服務之前取消預約，抑或是提供服務當日未出現，另外，若已接受的顧客大於可提供的資源，將會導致超額預訂的現象，並且給付賠償金給已接受但未提供服務的顧客。當顧客來到時，決策者需即時決定是否接受該顧客，對於決策者而言，在需求不確定的狀況下做決策是個困難的問題。根據Lai (2010)提出的模擬期望價差(simulated expected revenue gap, SERG)，本研究提出了模擬期望收益法(simulated expected revenue approach, SERA)。
本研究之旅館業首先探討單一房型以及單一房價的問題，第二種複雜度問題為單一房型以及多重房價，第三種複雜度為多重房型以及多重房價，由於本研究考慮升等，第三種複雜度問題可再細分為兩種類型作探討，第一種類型不允許顧客在入住期間更換房間類型，第二種類型允許顧客在入住期間換房間類型。根據實驗結果，模擬期望收益法在航空業以及旅館業所獲得的營收與完美資訊下所獲得的最佳營收差距最小，另外，在不同的控制因子水準下，模擬期望收益法表現穩定。

關鍵字：旅館業管理；航空業管理；非同質性卜瓦松過程；取消預約；放棄預約；超額預訂；升等；決策支援系統。

This study describes the stochastic problem of allocating finite capacity to booking requests with non-homogeneous Poisson processes with the consideration of cancellation, no-show, and overbooking and investigates the dynamic acceptance-or-rejection decision method for the revenue management of airline and hotel industries. In the airline problem, there is more than one fare class. For the hotel problem, multiple room classes, multiple price classes, and multiple stay lengths are considered. The decision maker would like to select the requests that customers are willing to pay a higher price by rejecting a current lower price customer, which may result in unused capacity. In addition, there is a possibility that an accepted request will be cancelled at a late time or even will not show up at the end time of booking horizon. If there are more requests than the available capacity at the departure time or check-in day, an overbooking penalty has to be paid for an unfulfilled customer. With the uncertain future demand arrivals, a correct acceptance-or-rejection decision for a current arrival request has to be made rapidly. This study proposes simulated expected revenue approach (SERA) that is modified from simulated expected revenue gap (SERG) originally proposed by Lai (2010).
In the hotel problem, there are three different levels of problem complexities in this study. The first level considers single room class with single price class, and the second level involves single room class with multiple price classes. The third level considers multiple room classes with multiple price classes. In addition, there are two problems with two different assumptions in the third level due to allowing upgrading. In the first case, changing room during a customer’s stay is allowed. In the second case, changing room during a customer’s stay is prohibited. Based on the experiment results, SERA performs the best among all the tested approaches. Moreover, SERA is very robust under various problem conditions.

Keywords: hotel management; airline management; non-homogeneous Poisson process; cancellation; no-show; overbooking; upgrade; decision support system.

致謝辭 I
摘要 II
Abstract III
TABLE OF CONTENTS V
LIST OF FIGURES VII
LIST OF TABLES IX
1. Introduction 1
1.1 The characteristics of revenue management 1
1.2 The difficulties of revenue management 3
1.3 The details of airline and hotel problems 5
2. Literature Review 7
2.1. Airline industry 7
2.2. Hotel industry 8
3. Proposed method 13
3.1. Airline industry 13
3.1.1 Concepts and assumptions 13
3.1.2. Notations 14
3.2. Hotel industry 16
3.2.1 Concepts and assumptions 16
3.2.2. Notations 18
3.3. Simulated expected revenue decision procedure 20
3.3.1. Decision under perfect information 21
3.3.2. Approximation and Estimated of the expected revenue under ODUPI 29
3.3.3. The SERA decision procedure 33
3.3.4. Discussion of the three different problem complexities. 35
4. Computational Experiments and Analysis 37
4.1. Compared approaches 37
4.2. Airline Industry 39
4.2.1. Experiment Design 39
4.2.2. Experiment Analysis 42
4.3. Hotel Industry 49
4.3.1. Experiment Design 49
4.3.2. Single Room Class and Single Price Class 52
4.3.3. Single Room Class and Multiple Price Classes 58
4.3.4. Multiple-Price Multiple-Room-Classes without Room Changes 65
4.3.5. Multiple-Price Multiple-Room-Classes with Room Changes 72
5. Conclusion and future research 78
Reference 79

American Airlines Annual Report. (1987), “The Art of Managing Yield”, pp. 22-25.
Baker, T. K. and Collier, D. A. (1999), “A Comparative Revenue Analysis of Hotel Yield Management Heuristics”, Decision Sciences, Vol. 30, No. 1, pp. 239-263.
Balakrishnan, N., Patterson, J.W., and Sridharan, V., (1999), “Robustness of capacity rationing policies”, European Journal of Operational Research, Vol. 115, No.2, pp. 328–338.
Balakrishnan, N., Sridharan, V., and Patterson, J.W., (1996), “Rationing capacity between two product classes”, Decision Sciences, Vol. 27, No.2, pp.185–214.
Belobaba, P. P. (1989), “Application of a Probabilistic Decision Model to Airline Seat Inventory Control”, Operations Research, Vol. 37, No. 2, pp. 183-197.
Bertsimas, D. and Popescu, I. (2003), “Revenue Management in a Dynamic Network Environment”, Transportation Science, Vol. 37, No. 3, pp. 257-277.
Bitran, G. R. and Mondschein, S. V. (1995), “An Application of Yield Management to the Hotel Industry Considering Multiple Day Stays”, Operations Research, Vol. 43, No. 3, pp. 427-433.
Chiang, W.-C., Chen, J., and Xu, X. (2007), “An overview of research on revenue management: current issues and future research”, International Journal Revenue Manage, Vol. 1, No. 1, pp. 97-128.
Fink, A. and Reiners, T. (2006), “Modeling and Solving the Short-term Car Rental Logistics Problem”, Transportation Research Part E-Logistics and Transportation Review, Vol. 42, No. 4, pp. 272-292.
Goldman, P., Freling, R., Pak, K., and Piersma, N. (2002), “Models and Techniques for Hotel Revenue Management Using a Rolling Horizon”, Journal of Revenue and Pricing Management, Vol. 1, No. 3, pp. 207-219.
Guadix, J., Cortés, P., Onieva, L., and Muñuzuri, J. (2010), “Technology Revenue Management System for Customer Groups in Hotels”, Journal of Business Research, Vol. 63, No. 5, pp. 519-527.
Heo, C. Y. and Lee, S. (2009), “Application of Revenue Management Practices to the Theme Park Industry”, International Journal of Hospitality Management, Vol. 28, No. 3, pp. 446-453.
Hung, Y.-F. and Chen, C. -H. (2013), “An effective dynamic decision policy for the revenue management of an airline flight”, Int. J. Production Economics, Vol.144, pp.440-450.
Hung, Y.-F. and Lai, W. -L. (2010), “A Room Rationing Decision Method for Multiple Room Class Hotels”, Unpublished paper, Department of Industrial Engineering and Engineering Management, National TsingHua University.
Kimes, S. E. (1989), “The Basic of Yield Management”, The Cornell Hotel and Restaurant Administration Quarterly, Vol. 30, No. 3, pp. 14-19.
Lai, K. -K. and Ng, W.-L. (2005), “A Stochastic Approach to Hotel Revenue Optimization”, Computer and Operations Research, Vol. 32, No. 5, pp. 1059-1072.
Lee, T. C., M. Hersh, (1993), “A model for dynamic airline seat inventory control with multiple seat bookings”, Transportation Science, Vol.27, No.3, pp.252-265.
Littlewood, K., (1972), “Forecasting and control of passenger bookings”, AGIFORS Symposium Proceeding, Vol. 12, pp.95-117.
Liu, S., Lai, K.-K., Dong, J., and Wang, S.-Y. (2006), “A Stochastic Approach to Hotel Revenue Management Considering Multiple-Day Stays”, International Journal of Information Technology and Decision Making, Vol. 5, No. 3, pp. 545-556.
Moon, G. Y., Hwi, Y. L., and Yoon, S.S., (2012) “Linear approximation approach for a stochastic seat allocation problem with cancellation & refund policy in airlines”, Journal of Air Transport Management, Vol.23, pp.41-46.
Pak, K., N. Piersma, (2002).” An overview of OR techniques for airline revenue management”, Statistica Neerlandica, Vol. 56, No.4, pp.479-495.
Subramanian, J., S. Stidham, Jr, C. J. Lautenbacher, (1999), “Airline yield management with overbooking , cancellations, and no-show”, Transportation Science, Vol.33, No.2, pp.147-167.
Weatherford, L. R. (1995), “Length of Stay Heuristics: Do They Really Make a Difference?”, The Cornell Hotel and Restaurant Administration Quarterly, Vol. 36, No. 6, pp. 70-79.
Weatherford, L. R. and Bodily, S. E. (1992), “A Taxonomy and Research Overview of Perishable-Asset Revenue Management: Yield Management, Overbooking, and Pricing”, Operations Research, Vol. 40, No. 5, pp. 831-844.
Youyi, F., and Baichun, X., (2006), “A continuous-time seat control model for single-leg flights with no-shows and optimal overbooking upper bound”, European Journal of Operational Research, Vol.174, pp.1298-1316.