Optimal pricing and seat allocation in the airline industry under the market competition

The current practice of revenue management is either quantity based or price based. A quantity based revenue management is most commonly observed in the airline industry; whereas a price based revenue management is practiced in retail enterprises. Recent improvement of information technology has not only increased the market size, but also has increased market competition. In a competitive environment customers choose among substitutable products depending on several rationalities, however a paramount factor in most selections is price. This thesis investigates pricing issue in revenue management and makes three contributions. First, price based revenue management is studied in the airline industry in a competitive market. Airlines compete for customers using their fare pricing strategies while having fixed capacity allocated in each fare class. The demand for each fare class of an airline is dependent on its fare price and the fare price offered by rival airline(s). A game theoretic approach is used to address the problem assuming both the deterministic and stochastic price sensitive customer demand for each fare class. The existence and uniqueness of Nash equilibrium for the game is shown for both deterministic and stochastic demands. A sensitivity analysis is carried out to determine fare pricing in each fare class considering various situations in the case of deterministic demand. The analysis is further extended to stochastic price sensitive demand, and a sensitivity analysis of the fare prices for each fare class is also reported. Second, an integrated approach to price and quantity based revenue management with an application to the airline industry is presented. The models proposed enable joint control of fare pricing and seat allocation in a duopoly competitive market. Both non cooperative and cooperative bargaining games are studied. Numerical experimentation is performed to study both competitive and cooperative fare pricing along with seat inventory control assuming a nested control on booking limits. In the case of a non cooperative game, Nash equilibrium for the competing airlines is determined assuming both symmetric and asymmetric market competition. A sensitivity analysis based on a statistical design of experiments is also presented to study the behavior of the game. Statistical evidence is established which shows that cooperation improves the revenue to the competing airlines. Lastly, a distribution free approach for pricing in revenue management is explored. The approach assumes the worst possible demand distribution and optimizes the lower bound estimate on revenue, while jointly controlling the price and capacity. The approach is first addressed to revenue management's most commonly observed standard newsvendor problem. Extensions to the problem are identified which can be applied to airline industry. Later the analysis is extended to consider the following cases: a shortage cost penalty; a holding and shortage cost; a recourse cost, with a second purchasing opportunity; and the case of random yields. An application of the approach is also suggested to capacity constrained industries facing restrictions such as limited budget. A numerical study reveals that the approach results in a near optimal estimate on revenue. Using a statistical comparison it is also shown that the outcomes of the standard newsvendor problem are significantly different than its extension

airline revenue management

With the increasing interest in decision support systems and the continuous advance of computer science, revenue management is a discipline which has received a great deal of interest in recent years. Although revenue management has seen many new applications throughout the years, the main focus of research continues to be the airline industry. Ever since Littlewood (1972) first proposed a solution method for the airline revenue management problem, a variety of solution methods have been introduced. In this paper we will give an overview of the solution methods presented throughout the literature.revenue management;seat inventory control;OR techniques;mathematical programming

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

User subscription-based resource management for Desktop-as-a-Service platforms

The Desktop-as-a-Service (DaaS) idiom consists of utilizing a cloud or other server infrastructure to host the user's desktop environment as a virtual desktop. Typical for cloud and DaaS services is the pay-as-you-go pricing model in combination with the availability of multiple subscription types to accommodate the needs of the users. However, optimal cost-efficient allocation of the virtual desktops to the infrastructure proves to be a combinatorial NP-hard problem, for which a heuristic is presented in the current article. We present a cost model for the DaaS service, from which a revenue of different configurations of virtual desktops to the servers can be derived. In this cost model, both subscription fee and penalties for degraded service are recorded, that are described in service-level agreements (SLAs) between the service provider and the users, and make realistic assumptions that different subscription types result in particular SLA contracts. The heuristic proposed states that for a given user base for which the virtual desktops (VDs) must be hosted, the VDs should be spread evenly over the infrastructure. Experiments through discrete event simulation show that this heuristic yields an approximation within 1 % of the theoretically achievable revenue

Project portfolio management: capacity allocation, downsizing decisions and sequencing rules.

This paper aims to gain insight into capacity allocation, downsizing decisions and sequencing rules when managing a portfolio of projects. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. In previous work, we have determined the amount of critical capacity that is optimally allocated to concurrently executed projects with deterministic or stochastic workloads when the impact of downsizing is known. In this paper, we extend this view with the possibility of sequential processing, which implies that a complete order is imposed on the projects. When projects are sequenced instead of executed in parallel, two effects come into play: firstly, unused capacity can be shifted to later projects in the same period; and secondly, reinvestment revenues gain importance because of the differences in realization time of the sequenced projects. When project workloads are known, only the second effect counts; when project workloads are stochastic, however, the project's capacity usage is uncertain so that unused capacity can be shifted to later projects in the same period. In this case, both effects need to be taken into account. In this paper, we determine optimal sequencing rules when the selection and capacity-allocation decisions for a set of projects have already been made. We also consider a combination of parallel and sequential planning and we perform simulation experiments that confirm the appropriateness of our capacity-allocation methods.Project portfolio management; Downsizing; Sequencing;

A Stochastic Dynamic Programming Approach to Revenue Management in a Make-to-Stock Production System

In this paper, we consider a make-to-stock production system with known exogenous replenishments and multiple customer classes. The objective is to maximize profit over the planning horizon by deciding whether to accept or reject a given order, in anticipation of more profitable future orders. What distinguishes this setup from classical airline revenue management problems is the explicit consideration of past and future replenishments and the integration of inventory holding and backlogging costs. If stock is on-hand, orders can be fulfilled immediately, backlogged or rejected. In shortage situations, orders can be either rejected or backlogged to be fulfilled from future arriving supply. The described decision problem occurs in many practical settings, notably in make-to-stock production systems, in which production planning is performed on a mid-term level, based on aggregated demand forecasts. In the short term, acceptance decisions about incoming orders are then made according to stock on-hand and scheduled production quantities. We model this problem as a stochastic dynamic program and characterize its optimal policy. It turns out that the optimal fulfillment policy has a relatively simple structure and is easy to implement. We evaluate this policy numerically and find that it systematically outperforms common current fulfillment policies, such as first-come-first-served and deterministic optimization.revenue management;advanced planning systems;make-to-stock production;order fulfillment

Capacity allocation and downsizing decisions in project portfolio management.

This paper aims to gain insight into capacity allocation and downsizing decisions in project portfolio management. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. We first determine the amount of critical capacity that is optimally allocated to strategic projects with deterministic or stochastic workloads for a single-period problem when the impact of downsizing is known. In order to solve the multi-period problem, we have modeled the behavior of the portfolio in subsequent periods as a single project for which the return on investment can be estimated. Secondly, we investigate how the scarcity of resources affects the (expected) value of projects. The independent (expected) project value is calculated under the assumption of unlimited capacity; in contrast, the dependent (expected) project value incorporates the resource constraints. We find that the dependent project value is equal to the independent project value when the return on investment of the portfolio is sufficiently low. In addition, we determine the relation between the return on investment of the portfolio and the value of a project and conclude that the impact of resource scarcity on the value of a project cannot be fully captured by the common financial practice of adapting the discount rate with the estimated return on investment.Project portfolio management; Downsizing; Stochastic workload;

Models and Techniques for Hotel Revenue Management Using a Roling Horizon

AbstractThis paper studies decision rules for accepting reservations for stays in a hotel based on deterministic and stochastic mathematical programming techniques. Booking control strategies are constructed that include ideas for nesting, booking limits and bid prices. We allow for multiple day stays. Instead of optimizing a decision period consisting of a fixed set of target booking days, we simultaneously optimize the complete range of target booking dates that are open for booking at the moment of optimization. This yields a rolling horizon of overlapping decision periods, which will conveniently capture the effects of overlapping stays.mathematical programming;Revenue Management;yield management