Seat allocation problem for parallel trains considering advanced purchase discount
|關鍵字:||鐵路;營收管理;座位控管;折扣機制;平行班次;線性規劃;Railway Industry;Revenue Management;Seat Inventory Control;Discount Mechanism;Parallel Train;Linear Programming|
Railways have been an important mode of transportation for years, and it is mostly preferred by many governments and organizations from the aspect of sustainability. Thus, the speed and capacity of many railway systems has been upgraded, due to the development of technology and the investment of massive resources. However, relatively less attention has been paid to the management skills and techniques of operating railway systems. In particular, unlike the airline industry with sophisticated revenue management (RM) systems to generate considerable extra revenue, the railway industry tends to conservative about implementing RM to exercises market segmentation and price differentiation. Meanwhile, not much research work has been done to evaluate the effectiveness of RM in the railway industry and develop suitable models to provide better decision support. This study focuses on the seat inventory control for a railways system with multiple original-destination (OD) pairs sharing the train system capacity. Due to the instinct stochastic feature of railway demand, this decision model is more complicated when compared with the traditional simple seat allocation model. In particular, the behavior of demand may be more than one choice due to the discount mechanism and parallel trains, makes this RM problem much more challenging than traditional seat allocation models. This study plans to develop several static linear programming (LP) models that can generate the control decision capable of dealing with heterogeneous demand. Moreover, different scenarios are designed in the simulation experiment to validate the applicability of the developed models and solution approaches, based on these simulated revenues. Finally, based on the numerical experiment from Taiwan High Speed Rail Company (THSRC), the parallel model, which considers heterogeneous demand, has the best revenue outcome. This result proves that the parallel model is helpful for improving the revenue in a railway system.
|Appears in Collections:||Thesis|