Single-leg airline revenue management with overbooking

Airline revenue management is about identifying the maximum revenue seat allocation policies. Since a major loss in revenue results from cancellations and no-show passengers, over the years overbooking has received a significant attention in the literature. In this study, we propose new models for static and dynamic single-leg overbooking problems. In the static case, we introduce computationally tractable models that give upper and lower bounds for the optimal expected revenue. In the dynamic case, we propose a new dynamic programming model, which is based on two streams of arrivals. The first stream corresponds to the booking requests and the second stream represents the cancellations. We also conduct simulation experiments to illustrate the proposed models and the solution methods

The Forty-year History of Revenue Management: Bibliometric Analysis

This paper presents research trends, leading publishers, influential articles, and shifting concerns in the field of Revenue Management for over forty years based on bibliometric analysis. Bibliometric data was retrieved from Web of Science core collection with a well-defined strategy. The data was processed using Network Analysis Interface for Literature Studies Project scripts. Subject-wise and year-wise research trends were presented. The shifting concerns in RM in terms of topic, method, and domain were highlighted using keyword analysis. In general, RM showed an increasing number of published papers with exponential manner every year. The research core in RM covered the three major decisions in RM including pricing, quantity control, and structural decision. It was highlighted that RM’s concern has shifted from single-firm decision to be more consumer- and competition-centric. The data showed that the needs of empirical study and more advanced quantitative methods for complex and real-time problems were urged. In addition, the adoption of RM was extended for industries with semi-flexible capacity. The top influential publishers were Decision Sciences, Operations Research, Management Science, and Management Science Manufacturing & Service Operations Management

Dynamic Pricing through Sampling Based Optimization

In this paper we develop an approach to dynamic pricing that combines ideas from data-driven and robust optimization to address the uncertain and dynamic aspects of the problem. In our setting, a firm off ers multiple products to be sold over a fixed discrete time horizon. Each product sold consumes one or more resources, possibly sharing the same resources among di fferent products. The firm is given a fixed initial inventory of these resources and cannot replenish this inventory during the selling season. We assume there is uncertainty about the demand seen by the fi rm for each product and seek to determine a robust and dynamic pricing strategy that maximizes revenue over the time horizon. While the traditional robust optimization models are tractable, they give rise to static policies and are often too conservative. The main contribution of this paper is the exploration of closed-loop pricing policies for di fferent robust objectives, such as MaxMin, MinMax Regret and MaxMin Ratio. We introduce a sampling based optimization approach that can solve this problem in a tractable way, with a con fidence level and a robustness level based on the number of samples used. We will show how this methodology can be used for data-driven pricing or adapted for a random sampling optimization approach when limited information is known about the demand uncertainty. Finally, we compare the revenue performance of the di fferent models using numerical simulations, exploring the behavior of each model under diff erent sample sizes and sampling distributions.National Science Foundation (U.S.) (Grant 0556106-CMII)National Science Foundation (U.S.) (Grant 0824674-CMII)Singapore-MIT Allianc

Hotel sales and reservations planning

Includes bibliographical references (p. 26-27).Research partially supported by the Leaders for Manufacturing Program.Gabriel R. Bitran, Thin-Yin Leong

An Optimization Technique of Airlines\u27 Seat Inventory Management

This study consisted of a simulation to maximize an airline\u27s Origin- Destination revenues. It has been hypothesized that Linear Programming is capable of maximizing airlines\u27 networks revenues. Simulation was performed with Linear Interactive Discrete Optimizer (LINDO). This project was designed to consider different fares and its classes along with multi flight connections. The seats\u27 allocation process came out with the maximum possible revenues, and enough flexibility in terms of changing such allocation to work around competition and trends. Results came supportive to the hypothesis. Sensitivity analysis provided our model with a tool to modify and change different variables, like fares, without affecting the reached goal (maximized network revenues). Although, the utilized network is a portion of a real world one, this study should inspire revenue management departments build their own simulation based on this model. Conclusion and recommendations are submitted

A cryptographic cloud-based approach for the mitigation of the airline cargo cancellation problem

In order to keep in good long-term relationships with their main customers, Airline Cargo companies do not impose any fee for last minute cancellations of shipments. As a result, customers can book the same shipment on several cargo companies. Cargo companies try to balance cancellations by a corresponding volume of overbooking. However, the considerable uncertainty in the number of cancellations does not allow to fine-tune the optimal overbooking level, causing losses. In this work, we show how the deployment of cryptographic techniques, enabling the computation on private information of customers and companies data can improve the overall service chain, allowing for striking and enforcing better agreements. We propose a query system based on proxy re-encryption and show how the relevant information can be extracted, still preserving the privacy of customers\u2019 data. Furthermore, we provide a Game Theoretic model of the use case scenario and show that it allows a more accurate estimate of the cancellation rates. This supports the reduction of the uncertainty and allows to better tune the overbooking level

Cargo Revenue Management: Bid-Prices for a 0-1 Multi Knapsack Problem

Revenue management is the practice of selecting those customers that generate the maximum revenue from a fixed and perishable capacity. Cargo revenue management differs from the well-known passenger revenue management problem by the fact that its capacity constraint is 2-dimensional, i.e. weight and volume, and that the weight, volume and profit of each booking request are random and continuous variables. This leads to a multi-dimensional on-line knapsack problem. We show that a bid-price acceptance policy is asymptotically optimal if demand and capacity increase proportionally and the bid-prices are set correctly. We provide a heuristic to set the bid-prices based on a greedy algorithm for the multi-knapsack problem proposed by Rinnooy Kan et al. (1993). A test case shows that these bid-prices perform better than the traditional LP-based bid-prices that do not perform well at all for this problem

\u3ci\u3eThe Conference Proceedings of the 1998 Air Transport Research Group (ATRG) of the WCTR Society, Volume 1 \u3c/i\u3e

UNOAI Report 98-6https://digitalcommons.unomaha.edu/facultybooks/1154/thumbnail.jp

Examining The Influence Of Dependent Demand Arrivals On Patient Scheduling

This research examines the influence of batch appointments on patient scheduling systems. Batch appointments are characterized by multiple patients within a family desiring appointments within the same time frame