A network airline revenue management framework based on decomposition by origins and destinations

We propose a framework for solving airline revenue management problems on large networks, where the main concern is to allocate the flight leg capacities to customer requests under fixed class fares. This framework is based on a mathematical programming model that decomposes the network into origin-destination pairs so that each pair can be treated as a single flight leg problem. We first discuss that the proposed framework is quite generic in the sense that not only several well-known models from the literature fit into this framework but also many single flight leg models can be easily extended to a network setting through the prescribed construction. Then, we analyze the structure of the overall mathematical programming model and establish its relationship with other models frequently used in practice. The application of the proposed framework is illustrated through two examples based on static and dynamic single-leg models, respectively. These illustrative examples are then benchmarked against several existing methods on a set of real-life network problems

A Column Generation Algorithm for Choice-Based Network Revenue Management

In the last few years, there has been a trend to enrich traditional revenue management models built upon the independent demand paradigm by accounting for customer choice behavior. This extension involves both modeling and computational challenges. One way to describe choice behavior is to assume that each customer belongs to a segment, which is characterized by a consideration set, i.e., a subset of the products provided by the firm that a customer views as options. Customers choose a particular product according to a multinomial-logit criterion, a model widely used in the marketing literature. In this paper, we consider the choice-based, deterministic, linear programming model (CDLP) of Gallego et al. [6], and the follow-up dynamic programming (DP) decomposition heuristic of van Ryzin and Liu [16], and focus on the more general version of these models, where customers belong to overlapping segments. To solve the CDLP for real-size networks, we need to develop a column generation algorithm. We prove that the associated column generation subproblem is indeed NP-Complete, and propose a simple, greedy heuristic to overcome the complexity of an exact algorithm. Our computational results show that the heuristic is quite effective, and that the overall approach has good practical potential and leads to high quality solutions.Operations Management Working Papers Serie

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

On bounds for network revenue management

The Network Revenue Management problem can be formulated as a stochastic dynamic programming problem (DP or the\optimal" solution V *) whose exact solution is computationally intractable. Consequently, a number of heuristics have been proposed in the literature, the most popular of which are the deterministic linear programming (DLP) model, and a simulation based method, the randomized linear programming (RLP) model. Both methods give upper bounds on the optimal solution value (DLP and PHLP respectively). These bounds are used to provide control values that can be used in practice to make accept/deny decisions for booking requests. Recently Adelman [1] and Topaloglu [18] have proposed alternate upper bounds, the affine relaxation (AR) bound and the Lagrangian relaxation (LR) bound respectively, and showed that their bounds are tighter than the DLP bound. Tight bounds are of great interest as it appears from empirical studies and practical experience that models that give tighter bounds also lead to better controls (better in the sense that they lead to more revenue). In this paper we give tightened versions of three bounds, calling themsAR (strong Affine Relaxation), sLR (strong Lagrangian Relaxation) and sPHLP (strong Perfect Hindsight LP), and show relations between them. Speciffically, we show that the sPHLP bound is tighter than sLR bound and sAR bound is tighter than the LR bound. The techniques for deriving the sLR and sPHLP bounds can potentially be applied to other instances of weakly-coupled dynamic programming.revenue management, bid-prices, relaxations, bounds

A review of choice-based revenue management : theory and methods

Over the last fifteen years, the theory and practice of revenue management has experienced significant developments due to the need to incorporate customer choice behavior. In this paper, we portray these developments by reviewing the key literature on choice-based revenue management, specifically focusing on methodological publications of availability control over the years 2004–2017. For this purpose, we first state the choice-based network revenue management problem by formulating the underlying dynamic program, and structure the review according to its components and the resulting inherent challenges. In particular, we first focus on the demand modeling by giving an overview of popular choice models, discussing their properties, and describing estimation procedures relevant to choice-based revenue management. Second, we elaborate on assortment optimization, which is a fundamental component of the problem. Third, we describe recent developments on tackling the entire control problem. We also discuss the relation to dynamic pricing. Finally, we give directions for future research

A Mathematical Programming Framework for Network Capacity Control in Customer Choice-Based Revenue Management

RÉSUMÉ : Cette thèse est basée sur l'étude de différentes approches pour répondre à la problématique du contrôle de capacité pour les réseaux en gestion du revenu. Elle est composée de cinq chapitres. Le premier donne une vue d’ensemble de la thèse ainsi que la méthodologie suivie pour analyser chaque approche. Les trois chapitres suivants sont à mettre en lien avec des articles que nous avons soumis dans des revues internationales. Ils proposent de nouveaux modèles et algorithmes pour le contrôle de capacité en gestion du revenu. Les cinquième et sixième chapitres contiennent la conclusion et l’ouverture de la thèse. Nous décrivons, dans la suite, chaque chapitre plus précisément. Dans le chapitre deux, nous proposons une approche de programmation mathématique avec choix de clients afin d’estimer les bid prices variant dans le temps. Notre méthode permet de prendre facilement en compte les contraintes techniques et pratiques d’un système de réservation central contrairement aux solutions actuelles proposées dans la littérature. En plus d’avoir développé un filtre vérifiant la disponibilité de combinaisons de produits sous un contrôle par bid price, nous avons mis au point un algorithme de génération de colonnes où une puissante heuristique est utilisée pour résoudre le sous-Problème fractionnel qui est NP-difficile. Encore une fois nos résultats numériques sur des données simulées montrent que notre solution est meilleure que les approches actuelles. Dans le chapitre trois, nous développons une nouvelle méthode de programmation mathématique pour obtenir une allocation optimale des ressources avec un modèle de demande à choix non paramétriques. Notre méthode est alors complétement flexible et ne souffre pas des inefficacités des modèles paramétriques actuels comme ceux de type multinomial logit. Pour cela, nous avons modifié un algorithme de génération de colonnes afin de traiter efficacement des problèmes réels de grande taille. Nos résultats numériques montrent que notre méthode est meilleure que les méthodes de la littérature actuelle à la fois en qualité de la solution qu’en temps de résolution. Dans le chapitre quatre, nous analysons un nouveau programme mathématique avec choix de clients pour estimer des booking limits qui doivent respecter une hiérarchie (nesting) ainsi que des règles commerciales imposées par le système de réservation central. De la même manière qu’au chapitre précédent, nous identifions les combinaisons de produits respectant ou non la hiérarchie (nesting) fixée par la politique de contrôle et nous développons une heuristique basée sur la décomposition. En simulant le processus stochastique d’arrivée, nous montrons encore une fois l'efficacité de notre méthode pour résoudre des problèmes complexes.----------ABSTRACT : This dissertation, composed of five chapters, studies several policies concerned with the issue of capacity control in network revenue management. The first chapter provides an overview of the thesis, together with the general methodology used to analyze the control policies. In the next three chapters, each of which corresponding to a paper submitted to an international journal, we propose new models and algorithms for addressing network revenue management. The fifth and final chapter concludes the dissertation, opening avenues for further investigation. We now describe the content of each article in more detail. In Chapter 2, we propose a customer choice-based mathematical programming approach to estimate time-dependent bid prices. In contrast with most approaches in the literature, ours can easily accommodate technical and practical constraints imposed by central reservation systems. Besides developing a filter that checks the compatibility of feasible product combinations under bid price control, we develop a column generation algorithm where a powerful heuristic is used to solve the NP-hard fractional subproblem. Again, our computational results show, based on simulated data, that the new approach outperforms alternative approaches. In Chapter 3, we develop a new mathematical programming framework to derive optimal an optimal allocation of resources under a non-parametric choice model of demand. The implemented model is completely flexible and removes the inefficiencies of current parametric models, such as those of the ubiquitous multinomial logit. We develop for its solution a modified column generation algorithm that can efficiently address large scale, real world problems. Our computational results show that the new approach outperforms alternative approaches from the current literature, both in the terms of the quality of the solution and the required processing time. In Chapter 4, we analyze a novel customer choice-based mathematical program to estimate booking limits that are required to be nested, while simultaneously satisfying the business rules imposed by most central reservation system. Similar to what was accomplished in the previous chapter, we identify product combinations that are compatible (or not) with some nested control policy, and develop a decomposition-based heuristic algorithm. By simulating the stochastic arrival process, we again illustrate the efficiency of the method to tackle complex problems

Basics of Dynamic Programming for Revenue Management

International audienceThe Revenue Management (RM), namely the pricing and the inventory control of a perishable product, is usually used to improve services marketing efficiency. While booking a flight, the manager has to allocate seats to various fare classes. Then, he has to assess the consequence of a current decision on the future stream of revenue, i.e. accept an certain incoming reservation or wait for a possible higher fare demand, but later. Since its practice becomes omnipresent this last decade, this paper presents some basics of Dynamic Programming (DP) through the most common model, the dynamic discrete allocation of a resource to n fare classes. The properties of the opportunity cost of using a unit of a given capacity, the key of any RM optimizations, are studied in details

A Choice-Based Dynamic Programming Approach for Setting Opaque Prices

Opaque pricing is a form of pricing where certain characteristics of the product or service are hidden from the consumer until after purchase. In essence, opaque selling transforms a differentiated good into a commodity. Opaque pricing has become popular in service pricing as it allows firms to sell their differentiated product at higher prices to regular brand loyal customers while simultaneously selling to non-brand loyal customers at discounted prices. We use a nested logit model in combination with logistic regression and dynamic programming to illustrate how a service firm can optimally set prices on an opaque sales channel. The choice model allows the characterization of consumer trade-offs when purchasing opaque products while the dynamic programming approach allows the characterization of the optimal pricing policy as a function of inventory and time remaining. We compare optimal prices and expected revenues when dynamic pricing is restricted to daily price changes. We provide an illustrative example using data from an opaque selling mechanism (Hotwire.com) and a Washington DC-based hotel