The Simulation and Policy Analysis of Online Booking System for Taiwan Railways Administration

• Main Authors: Pai-Jui Chen, 陳栢睿
• Other Authors: Chih-Pon Chu
• Format: Others
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/c886ua

碩士 === 國立東華大學 === 企業管理學系 === 100 === Booking system plays an important role in perishable asset such as transportation industry, hotel industry and car rent industry. The concept of Revenue Management solves the problem of perishable asset by using forecasting, allocation, price discrimination and overbooking policy. However, Taiwan Railway Administration’s current system does not allow for price discrimination and overbooking policy. The problem here is resource allocation, we therefore focused on the volume of resource and the timing for the availability of resource. In this research, we constructed a simulation model and assume there are three kinds of customers namely, travel agent, businessman and normal customers, each with individual booking behavior in the booking system. We have three ticket-get date polices, two days, three days and five days. We also have three resource policies, policy 1 is full allowance in the beginning, policy 2 is half allowance in the beginning and full allowance after a week and policy 3 is extra 10% of resource allowance. We use simulation Arena 11, to simulate different population as our scenario and use four indexes: success booking rate, refund rate, cancel rate and failure rate to describe their conditions. We also use four indexes, revenue, society resource, same weight on revenue and society resource and value to measure the performance in each policy. The results showed out that on the value side, the best combination of policy is resource policy 2 with 5 days. The current get time policy is 2 days, if we cannot change the get time policy, we would rather choose the resource policy 2 than resource policy 1. We also recommend the regulation for refund that is similar to Japan, China or Korea as percentage of ticket price rather than a fix number. The best combination is also the 5 days and resource policy 3.