Stochastic programming for multiple-leg network revenue management

Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced probabilistic models. Recently suggested models put even more effort into demand forecasting. We will show that the dynamic booking process rather than the demand forecasts needs to be addressed. In particular the nesting strategies applied in booking control will counter-effect the profitability of advanced estimation of booking limits and bid-prices.simulation;revenue management;mathematical programming

Stochastic programming for multiple-leg network revenue management

Airline seat inventory control is a very profitable tool in the airline industry. Mathematical programming models provide booking limits or bid-prices for all itineraries and fare classes based on demand forecasts. But the actual revenue generated in the booking process fails to meet expectations. Simple deterministic models based on expected demand generate better revenue than more advanced probabilistic models. Recently suggested models put even more effort into demand forecasting. We will show that the dynamic booking process rather than the demand forecasts needs to be addressed. In particular the nesting strategies applied in booking control will counter-effect the profitability of advanced estimation of booking limits and bid-prices

TOWARDS AN EFFICIENT DECISION POLICY FOR CLOUD SERVICE PROVIDERS

Cloud service providers may face the problem of how to price infrastructure services and how this pricing may impact the resource utilization. One aspect of this problem is how Cloud service providers would decide to accept or reject requests for services when the resources for offering these services become scarce. A decision support policy called Customized Bid-Price Policy (CBPP) is proposed in this paper to decide efficiently, when a large number of services or complex services can be offered over a finite time horizon. This heuristic outperforms well-known policies, if bid prices cannot be updated frequently during incoming requests and an automated update of bid prices is required to achieve more accurate decisions. Since CBPP approximates the revenue offline before the requests occur, it has a low runtime compared to other approaches during the online phase. The performance is examined via simulation and the pre-eminence of CBPP is statistically proven

Re-Solving Stochastic Programming Models for Airline Revenue Management

We study some mathematical programming formulations for the origin-destination model in airline revenue management. In particular, we focus on the traditional probabilistic model proposed in the literature. The approach we study consists of solving a sequence of two-stage stochastic programs with simple recourse, which can be viewed as an approximation to a multi-stage stochastic programming formulation to the seat allocation problem. Our theoretical results show that the proposed approximation is robust, in the sense that solving more successive two-stage programs can never worsen the expected revenue obtained with the corresponding allocation policy. Although intuitive, such a property is known not to hold for the traditional deterministic linear programming model found in the literature. We also show that this property does not hold for some bid-price policies. In addition, we propose a heuristic method to choose the re-solving points, rather than re-solving at equally spaced times as customary. Numerical results are presented to illustrate the effectiveness of the proposed approach

Overbooking models for air cargo yield management

Master'sMASTER OF ENGINEERIN

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

Optimization models for joint airline pricing and seat inventory control : multiple products, multiple periods

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 153-157).Pricing and revenue management are two essential levers to optimize the sales of an airline's seat inventory and maximize revenues. Over the past few decades, they have generated a great deal of research but have typically been studied and optimized separately. On the one hand, the pricing process focused on demand segmentation and optimal fares, regardless of any capacity constraints. On the other hand, researchers in revenue management developed algorithms to set booking limits by fare product, given a set of fares and capacity constraints. This thesis develops several approaches to solve for the optimal fares and booking limits jointly and simultaneously. The underlying demand volume in an airline market is modeled as a function of the fares. We propose an initial approach to the two-product, two-period revenue optimization problem by first assuming that the demand is deterministic. We show that the booking limit on sales of the lower-priced product is unnecessary in this case, allowing us to simplify the optimization problem. We then develop a stochastic optimization model and analyze the combined impacts of fares and booking limits on the total number of accepted bookings when the underlying demand is uncertain. We demonstrate that this joint optimization approach can provide a 3-4% increase in revenues from a traditional pricing and revenue management practices. The stochastic model is then extended to the joint pricing and seat inventory control optimization problem for booking horizons involving more than two booking periods, as is the case in reality. A generalized methodology for optimization is presented, and we show that the complexity of the joint optimization problem increases substantially with the number of booking periods. We thus develop three heuristics. Simulations for a three-period problem show that all heuristics outperform the deterministic optimization model. In addition, two of the heuristics can provide revenues close to those obtained with the stochastic model. This thesis provides a basis for the integration of pricing and revenue management. The combined effects of fares and booking limits on the number of accepted bookings, and thus on the revenues, are explicitly taken into account in our joint optimization models. We showed that the proposed approaches can further enhance revenues.by Claire Cizaire.Ph.D

Revenue management in airline operations : booking systems and aircraft maintenance services

Although the principles of Revenue Management (RM) have vaguely been used in business for a long time, an increasing number of organizations are implementing well structured RM systems in the last few decades due to the developments in science and technology, especially in economics, statistics, operations research and computer science. The improvements in information and telecommunication technologies, wide use of Internet, rise of e-commerce and successful supply chain management strategies have enabled organizations to model and solve complex RM problems. This dissertation research concentrates on airlines, the earliest and leading user of RM. Today, airlines face serious financial problems due to the increasing costs and competition. They continuously explore new opportunities especially in terms of RM to make profit and survive. In this study, two problems are analyzed within this scope; airline booking process with adapted options approach and aircraft maintenance order control through RM. First; a new approach, financial options approach, is proposed to sell tickets in airline reservation systems. The options are used to overcome the uncertainty in air travel demand and competitors' actions. The seat inventory control problem is formulated with overbooking and embedded options respectively. Then a simulation study is conducted the potential of using options in airlines booking process. Accordingly, empirical results show that they present an opportunity both to utilize capacity more efficiently and to value seats more precisely compared to overbooking approach. Secondly; a peak load pricing concept is applied for aircraft maintenance order control problem. Aircraft maintenance centers face with peak loads in some seasons and the capacity is underutilized in other seasons. A peak load pricing model is proposed to shift some of the price elastic demand from peak seasons to off-peak seasons to balance demand and supply around the year. A dynamic programming algorithm is developed to solve the model and a code is written in C++. Results show that the model improves both annual capacity loading factors and revenues without causing a discomfort from the perspective of the customers. The details of both studies are presented in this dissertation research. [PUBLICATION ABSTRACT