A dynamic model for railway freight overbooking

In order to apply overbooking idea in Chinese railway freight industry to improve revenue, a Markov decision process(dynamic programming) model for railway freight reservation was formulated and the overbooking limit level was proposed as a control policy. However, computing the dynamic programming treatment needs six nested loops and this will be burdensome for real-world problems. To break through the calculation limit, the properties of value function were analyzed and the overbooking protection level was proposed to reduce the calculating quantity. The simulation experiments show that the overbooking protection level for the lower-fare class is higher than that for the higher-fare class, so the overbooking strategy is nested by fare class. Besides, by analyzing the influence on the overbooking strategy of freight arrival probability and cancellation probability, the proposed approach is efficient and also has a good application prospect in reality. Also, compared with the existing reservation(FCFS), the overbooking strategy performs better in the fields of vacancy reduction and revenue improvement.

A Part-Refund Model for Airline Overbooking Problem

In this paper, a mathematical model about the optimal booking level problem in airline overbooking rnanageanent is proposed. Firstly, the normal distribution is employed to approximate Poisson requests and binomial goshows. By calculating the mean loss, a model to describe the relationship between booking level and flight revenue is established. Then, an equation is deduced to decide the optimal booking level. Finally, a numerical example is utilized to illustrate the proposed model and discuss the impact of refund rate on the optimal booking level and the mean loss.

Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service requests.However,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste resources.Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization.First,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for processing.Different deployment locations have different resource utilization and average service response time.We want tofind a balanced solution that minimizes the average service response time of the IoT application while maximizing resource utilization.In this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment scheme.Finally,the simulation results show that the TDA can significantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical methods.The experimental results confirm that the perfor-mance of TDA is better than that of other two methods.

Optimal Overbooking Mechanism in Data Plan Sharing

For mobile devices users, the inconvenience caused by the fixed data plans becomes a popular issue. With the help of personal hotspot (pH), however, the smartphone users can share network connection with other users nearby. In this paper a data plan sharing platform with reservation mechanism is proposed. The users on this platform can sell their surplus data plan traffic to make profit. A Markov decision process model was formulated and analyzed for data plan sharing platform revenue management through overbooking mechanism. The results of experiment and simulation show that the platform revenue has a considerable growth using this model.

Airline Overbooking

This article deals with an optimal booking service system. According to the optimization object economic effects, we build an objective function with constraint conditions. Based on this function we prove the model so as to get it more conform to the common situation producer can im-prove the model so as to get it more conform to the common situation.