A booking limit model for railway considering advanced purchase discount and choice behavior
|關鍵字:||鐵路;營收管理;座位控管;折扣機制;選擇行為;線性規劃;Railway Industry;Revenue Management;Seat Inventory Control;Discount Mechanism;Choice Behavior;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 rail industry and develop suitable models to provide better decision support. This study aims to develop the decision model for seat inventory control about a railway system with multiple original-destination (OD) pairs sharing the train system capacity. Due the instinct stochastic feature of railway demand, this decision model is more complicated when compared with the traditional simple seat allocation model. In particular, give the introduction of the advanced purchase discount, the associated choice behavior of travelers and demand dependency must be taken into account in the decision models. This study develops several static linear programming (LP) models that can generate the control decisions capable of dealing with uncertain, heterogeneous, and inter-dependent 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. Based on the numerical experiment, the integrated model, which considers heterogeneous and uncertain of demand, has the best revenue outcome. Its revenues are higher than the revenues of other models by 0.2% to 21.2%. This promising result proves that the integrated model is helpful for improving the revenue in a railway system.