Revenue management

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Le Revenue management pour les clientèles de groupes

Le Revenue management pour les clientèles de groupes

I.​ ​MÉTHODOLOGIE  A.​ ​COMPRÉHENSION​ ​ET​ ​PROBLÉMATISATION​ ​DU​ ​SUJET  Comme je l’ai expliqué en introduction, le choix de ce sujet est lié à mon souhait de travailler dans le management hôtelier après mes études mais également au fait que la problématique du Revenue Management dans les hôtels soit d’actualité et en pleine expansion depuis quelques années. Dans un premier temps, je m’étais penchée sur plusieurs sujets, principalement des sujets liés à la communication et au développement des canaux de distribution dans l’hôtellerie. Tous ces thèmes sont liés au développement constant des nouvelles technologies et il me semblait intéressant d’aborder cet aspect si important de notre environnement. Mon idée de départ étant beaucoup trop large, elle n’allait pas aboutir à des résultats intéressants, c’est pourquoi je me suis intéressée à des techniques concrètes utilisées en hôtellerie pour finalement me centrer sur le Revenue Management qui m’a semblé être un sujet plus complexe, d’actualité et intéressant à traiter. Par ailleurs, le sujet du Revenue Management m’intéresse particulièrement pour son aspect commercial. En effet, je souhaite orienter ma carrière professionnelle vers des postes de commerciale et la notion de Yield est toujours présente. Ce mémoire me permet donc de compléter les connaissances acquises au cours de mes stages dans les services Sales & Marketing et Conferences & Events pour avoir une vision plus globale ​ ​et​ ​complète​ ​du​ ​système​ ​de​ ​vente​ ​dans​ ​l’hôtellerie​ ​sur​ ​les​ ​différents​ ​segments​ ​de​ ​clientèle.
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Airline revenue management for continuous pricing : class-based and classless methods

Airline revenue management for continuous pricing : class-based and classless methods

“Dynamic pricing” has become a popular phrase in the airline industry, although there is no consensus on what the term actually means. For the purpose of this thesis, the following definition will be used: “Firms practice dynamic pricing when they charge different customers different prices for the same product, as a function of an observable state of nature” (Wittman & Belobaba, 2018). This definition extends the dynamic pricing term to encompass nearly any revenue management method that controls fare class availability. Even an incredibly basic method, like arbitrarily deciding that 50% of the seats on the aircraft should be reserved for one fare class and the other 50% to another would fall under dynamic pricing by this definition since the price being is different for the two sets of customers, the product is the same, and the differences are based on an observable state of nature, which is how full the aircraft is. In addition to defining dynamic pricing, Wittman & Belobaba (2018) also define three mechanisms to implement dynamic pricing: assortment optimization, dynamic price adjustment, and continuous pricing.
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Algorithmic advancements in the practice of revenue management

Algorithmic advancements in the practice of revenue management

3.1.1 Our Contribution We make the following contribution, motivated by solving the Network Revenue Management problem given its economical implications for industries. Theoretical Hardness and Optimal Primal Dual Algorithm Given the challenge of leveraging extant forecasts and trusting their accuracy, especially for multi-product and multi-price cases, we consider the variant of analyzing the robust version of the problem. In particular we assume demand can take an adversarial form, and work on establishing a robust algorithm compared to the optimal performance achievable knowing demand beforehand. In effect the adversary can pick any demand sequence: arbitrary length, price spread and prod- ucts requested. The algorithm must make irrevocable sale decisions to guarantee a constant fraction of the best achievable revenue. We first establish a hardness result that extends the known result from [50] for the special case of single-leg revenue management. Then we pro- pose a general algorithm that achieves a constant fraction of revenue. By specifically tuning the hyper-parameters of our algorithm, we prove that we achieve optimal performance: i.e. that no algorithm, deterministic or randomized, can obtain a larger fraction of revenue on all instances. Our algorithm and its analysis relies on the primal-dual schema, that is we produce online primal and dual solutions, feasible for the offline problem, whose objectives are within a constant factor. This strong result highlights the theoretical contribution of our work. In practice other methods relying on distributional information about demand can be applied, but their performance can be tampered by the inaccuracies of the forecasts, which may be highly variable on the granular level.
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Stochastic Bilevel Models for Revenue Management in the Hotel Industry

Stochastic Bilevel Models for Revenue Management in the Hotel Industry

have also addressed similar issues using “opaque selling” 10 to maintain anonymity but still addressing the last-minute buyers segment, according to Jerath et al. (2010). 1.5 The Challenges of Hotel Revenue Management The new tendencies in HRM have pointed out to a more critical view of the almost 30 years of practice, as stated by Anderson and Xie (2010) but also to an even deeper critic of the vision of the models that do not consider LOS, multiple inventory share, conjoint overbooking, room availability guarantee, special events, and “Total RM” over mulitple properties and services, as presented by Ivanov and Zhechev (2012). In particular, those authors account not only for the complexity of effectively controlling the downward spiral of revenue and the current fencing, but also the case of long-stay booking, as Ling et al. (2012) describe, or multiply-day booking in periods of demand shortage. Indeed, in a world with excess of offers in lodging, rooms become a simple commodity. On the other hand, the challenges behind managing a more individualized demand, as highlighted by Cross et al. (2009), lead to an urgent re-specialization of HRM focused on choice-driven-pricing and service-pricing, and require a close collaboration with marketing as suggested by Kwortnik and Vosburgh (2007); Victorino et al. (2009); Verma (2010); Sengupta and Dev (2011).
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Adaptive robust optimization with applications in inventory and revenue management

Adaptive robust optimization with applications in inventory and revenue management

stantially reduce the optimality gap, while cubic policies (under more computational requirements) were always within 1% of the optimal solution. Finally, Chapter 4 considered a different version of a multi-period dynamical sys- tem, arising in the context of dynamic pricing applications in revenue management.For the multi-product case, under a linear demand function, we proposed a distribution- ally robust model for the uncertainties, and argued how it can be constructed from limited historical data. We then considered polynomial pricing policies parameter- ized directly in the observed model mis-specifications, and showed how these can be computed by solving second-order conic or semidefinite programming problems. Ex- tensive simulation results on both real and synthetic data allowed us to conclude that considering adjustable policies (versus open-loop formulations) considerably improves the quality of the objective, and yields pricing policies that are competitive with some of the popular heuristics in the literature.
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Competitive impacts of continuous pricing mechanisms in airline revenue management

Competitive impacts of continuous pricing mechanisms in airline revenue management

2.3 Towards Competitor-Aware Revenue Management Traditional revenue management models have generally assumed a monopolistic en- vironment in which all customers are perfectly loyal, i.e. a passenger who previously booked with a particular airline will continue to do so in the future. Current RM op- timization models do not explicitly consider the possibility of losing a passenger to a competitor with a more attractive offer because of a lower fare or better path/schedule quality. Competitive effects are only indirectly and passively accounted for through feedback with the forecaster, but the RM model does not actively take corrective me- asures to a competitor’s actions. In the Futures series of the Journal of Revenue and Pricing Management, several former RM VPs have foreshadowed the move towards so-called “competitor-aware” revenue management. Nason ( 2007 ) from American Air- lines argued that “RM systems need to get much better at knowing the competition’s price and incorporating it into the calculation of future demand and the elasticity of demand”. Similarly, Cary ( 2004 ) at United Airlines described pricing as “almost entirely outwardly focused on the actions and reactions of competitors” and reve- nue management as “almost entirely inwardly focused on the patterns and trends in historical demand data”.
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Airline revenue management with dynamic offers : bundling flights and ancillary services

Airline revenue management with dynamic offers : bundling flights and ancillary services

The functionality of PODS has been extended beyond its core itinerary and fare choice model over the years. For dynamic offer generation, two extensions are particularly relevant: ∙ Dynamic Price Adjustment: Traditional revenue management optimizers in PODS set the number of seats available for sale in each fare class. Each fare class has a pre-determined (filed or published ) price. PODS has a Dynamic Price Adjustment capability based on research on continuous pricing by Wittman and Belobaba (2018), which allows an airline to dynamically adjust the flight price away from the pre-determined price determined by the revenue management optimizer to any price within a continuous range determined by bounds. This adjustment could vary for each customer segment with a different willingness- to-pay (i.e. business or leisure). Probabilistic Fare-Based Dynamic Availability (PFDynA) is one example of a heuristic to optimize the adjusted continuous price for a customer segment, which is implemented in PODS (Wittman, 2018). ∙ Ancillary Choice: In PODS, airlines can offer ancillary services in addition to the flight. The passengers choose among itineraries, fare classes and ancillary services according to an integrated passenger choice model first introduced by Bockelie and Belobaba (2017). Besides the sequential and simultaneous choice behaviors described in the paper, passengers can also behave according to con- current choice behavior, in which they compare the flight and ancillary prices rationally against their separately drawn flight and ancillary willingness-to-pay. This behavior matches the concurrent choice assumption (cf. Chapter 3.2.2) used in the DOG pricing algorithm exactly. At the end of the simulation, PODS reports ancillary purchases, ancillary revenues and costs for each airline separately.
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A competitive approach to airline revenue management

A competitive approach to airline revenue management

132 We also provided a review of past simulations made using the PODS simulator. The main purpose of these simulations was not to focus on the competition between revenue management systems, but to test whether some suggested improvements would work in competitive environments. We grouped their results and reached conclusions on competitive interactions. An airline improving its revenue management system most of the time leads to an increase of its revenues at the expense of its competitors. Yet one cannot speak of a zero- sum game as the increase in the airline’s revenues is higher than the decrease in revenue of its competitors. It spills less high-fare class and more low-fare class passengers to competitors. Not only does this improve the leading airline’s fare class mix and revenues, it also shifts the demand experienced by the competitor towards low fare classes. No matter its optimization method, the latter ends with a worse fare class mix and lesser revenues. Conversely, when a tested revenue management technique happens to decrease the innovating airline’s revenues, it usually increases the passive airline’s revenues by having improved its mix of demand through spill. When both airlines improve their revenue management system, both benefit. There is no long-term advantage of being the first or the last to move towards the improved revenue management system.
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Revenue Management for transport service providers in Physical Internet : freight carriers as case

Revenue Management for transport service providers in Physical Internet : freight carriers as case

(1) Study systematically the Revenue Management in a highly dynamic environment. Originating from the airline industry, Revenue Management has been applied in many different industries. For example, in hotels and rental business. Moreover, more and more actors in freight transport industries adopt RM to improve their revenue. There is also an arising number of researches focused on the application of RM in freight transport. RM is mainly used to respond to the fluctuation in the market and better utilize resources’ capacity but the current logistics networks are not as dynamic as PI, which is a highly interconnected network of different logistics networks. PI is an innovative logistic concept that aims to integrate current logistics networks in an open global logistic system thus concentrating demands in hubs. Under this context, in PI, there will be abundant and frequent requests flows, which is much more dynamic than the transport demand in the traditional logistics networks. Besides, as discussed in Chapter 2, there are few researches on the application of RM in road freight transport. Therefore, the work in this thesis does not only contribute to the research on RM in highly dynamic network but also on the RM in road freight segment.
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Revenue Management for transport service providers in Physical Internet: freight carriers as case

Revenue Management for transport service providers in Physical Internet: freight carriers as case

(1) Study systematically the Revenue Management in a highly dynamic environment. Originating from the airline industry, Revenue Management has been applied in many different industries. For example, in hotels and rental business. Moreover, more and more actors in freight transport industries adopt RM to improve their revenue. There is also an arising number of researches focused on the application of RM in freight transport. RM is mainly used to respond to the fluctuation in the market and better utilize resources’ capacity but the current logistics networks are not as dynamic as PI, which is a highly interconnected network of different logistics networks. PI is an innovative logistic concept that aims to integrate current logistics networks in an open global logistic system thus concentrating demands in hubs. Under this context, in PI, there will be abundant and frequent requests flows, which is much more dynamic than the transport demand in the traditional logistics networks. Besides, as discussed in Chapter 2, there are few researches on the application of RM in road freight transport. Therefore, the work in this thesis does not only contribute to the research on RM in highly dynamic network but also on the RM in road freight segment.
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A Mathematical Programming Framework for Network Capacity Control in Customer Choice-Based Revenue Management

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

3.2 Choice modelling In this section, we briefly survey choice modelling issues. Indeed, a key issue in network revenue management is that of estimating the probability P j (S) that a product j be selec- ted by an arriving customer, given that a set S of products is on offer. Two main classes of models have been proposed for its solution. Parametric choice models are built upon the Random Utility Maximization paradigm (McFadden (2000)), whereby products are assigned attributes, and customers select the product that maximizes their own utility, expressed as a weighted sum of the attributes’ values. Depending on the statistical model underlying the selection process, one derives a variety of models : multinomial logit (MNL), nested logit, mixed logit, probit, generalized extreme value, etc. For instance, in the MNL model, which is widely used in marketing and economics (Train (1986)), customers belong to predefined segments characterized by a weight vector v associated with products’ attributes. The pro- bability of selecting product j is then set to the ratio of that product’s preference for the customer over the sum of all products’ preferences.
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Basics of Dynamic Programming for Revenue Management

Basics of Dynamic Programming for Revenue Management

Jean Michel Chapuis * Abstract: The 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.
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Demand Forecasting in Revenue Management Systems

Demand Forecasting in Revenue Management Systems

In the context of demand forecasting in Revenue Management (RM), forecasting methods could be divided into two main categories, statistical based and mathematical programming based techniques. Statistical methods of forecasting examine historical data to extract un- derlying process on which we can predict future trends. The selection of forecasting methods depends on several factors, such as the forecast format required, the availability of data, the desired accuracy and the ease of operation. Although these statistical methods are vastly ap- plied in demand forecasting, they have some drawbacks that motivate us to turn our attention to Artificial Neural Network (ANN) Devoto et al. (2002) Chung et Lee (2002). For example, time series models are described as mathematical processes that can be extended into the fu- ture. Despite the capabilities of this approach in transportation, the models cannot respond rapidly to sudden changes in bookings and cancellations. Sudden changes in the demand of each product happen as soon as one product is no longer available as a result of capacity limi- tations Montgomery et al. (2008). They also permit the forecast to take recent demand over earlier demand into account. However, the proper selection of past periods to use is a key decision that can be subjective.
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Cargo revenue management for space logistics

Cargo revenue management for space logistics

Like in the airline and cargo examples, in space logistics revenue management can be used to allocate cargo delivery capacity (i.e. cargo mass and volume) in order [r]

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Demand driven dispatch and revenue management

Demand driven dispatch and revenue management

In the fleet optimizer, operating profit is defined as follows: revenue contribution minus aircraft operating costs. Note that the operating profit that is reported from here on is not the same as total system revenue (which included an additional 40% of non-aircraft operating expenses when scaling costs).There are of course many ways to define flight leg profitability, as discussed in Baldanza (1999) and others. None of them are perfect as both costs and revenues must be (arbitrarily) allocated to legs. Considerations as to whether or not a decision is short-term or long-term are also important as to what costs should be included. For the sake of simplicity, block hour costs are the only costs used. They include fuel costs, crew costs, allocated maintenance costs, and allocated ownership costs. Aircraft operating costs are the most relevant costs as they depend the most on what flight leg an aircraft is ultimately assigned to fly. Costs associated with airport servicing, etc. will likely be less dependent on D³, as all aircraft and all airports will experience the same number of operations with aircraft of the same type. Therefore, these costs are less relevant when the optimization technique is considering only incremental costs. Variable passenger service costs are relevant because D³ increases RPMs significantly. However, for simplicity, these costs are ignored, as is customary with revenue management itself.
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Airline revenue management under alternative fare structures

Airline revenue management under alternative fare structures

The first chapter of this thesis gives a brief description of airline revenue management methods and possible variations in performance given changes in the fare stru[r]

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Effect of central transfers on municipalities' own revenue mobilization: Do conflict and local revenue management matter?

Effect of central transfers on municipalities' own revenue mobilization: Do conflict and local revenue management matter?

3. Fiscal decentralization in Côte d’Ivoire 3.1. Overview of Côte d’Ivoire Côte d’Ivoire is a sub-Saharan African country; more than 54 percent of its population lives in urban areas ( World Bank , 2015 ). Since 1980, Côte d’Ivoire has attempted to implement decentralization by transferring responsibility for expenditure and revenue-raising to local governments, with the aim of improving effectiveness and efficiency in the delivery of public services. From 2001 to 2008 the country experienced a political conflict marked by sporadic events of different intensity across the municipalities. A peace agreement was signed by all political parties, and marked the end of tension in 2007. Both sides agreed to a free and fair general election to be held in 2008 8 . This agreement might have induced a change in the behavior of municipalities. Thus, the study considers the post conflict period starting from 2008 . The revenue structure of local government in Côte d’Ivoire is largely inherited from the colonial period. Law No. 55-1489 of 18 November 1955 established municipalities in Abidjan, Bouaké, and Grand Bassam, but they did not have financial autonomy. After independence in 1960, decentralization, especially the financial autonomy of municipalities, was clearly not a priority for the central government. Although municipal council members and mayors were elected, the central government only started the process of decentralization under Law No. 80-1162 of 17 October 1980. This law defined a specific status and electoral regime for munic- ipalities, and created 37 municipal councils in addition to Abidjan. In 2000, the government adopted a new constitution, which sets out the principles of administration and financial au- tonomy of local authorities. This constitution divides the country into a multi-tiered system with 19 regions which are sub-divided into 58 départements led by département councils,
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Ancillary revenues in the airline industry : impacts on revenue management and distribution systems

Ancillary revenues in the airline industry : impacts on revenue management and distribution systems

With standard forecasting, Availability Adjustment results in positive revenue gains of +0.5% to +1.0% because the extra load obtained is beneficial when load factor is low overall. Ex- tra bookings are accepted early in the booking curve, which still allows the RM optimizer to close down in the middle time frames and capture higher value demand. This is in contrast to Input Adjustment (all demand levels) and Availability Adjustment in medium and high demand, where incorporating ancillary revenue led to revenue loss. In those scenarios, the rev- enue losses from spiral down were too large and offset the gains obtained from Class 6 bookings. With hybrid forecasting and fare adjustment, Availability Adjustment resulted in revenue gaoins from +0.4% to +0.9%, which is slightly higher than the gains with Input Adjustment and HF/FA. Similarly with Input Adjustment, HF/FA results in positive revenue gains because it models passenger sell up and closes down classes in the middle time frames before Class 6 AP. So far, tests have been run assuming airline estimates on a market and class level basis. Based on estimates of mean ancillary revenue potential, Airline incorporates these estimates within their RM input fares by modifying the RM optimizer or in availability fares only. The next area of testing explores the scenario that Airline 1 has estimates of ancillary revenue potential by each individual passenger, which represents an unrealistic and infeasible circumstance. Although airlines do not currently have the capacity to estimate on this level, it is possible to test this level of estimation in PODS and obtain a theoretically maximum possible incremental revenue gain.
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Capacity management schemes for dual cabin aircraft : airline revenue management insights

Capacity management schemes for dual cabin aircraft : airline revenue management insights

73 Figure 4-3: BSM Model - Proportional Variation Revenue on legs operated by A/C Type A – OD-Control  Aircraft Type ‘B’ (baseline Economy Cabin 200 seats – Premium Cabin 24 seats) The revenue changes estimated by using the dual cabin BSM for evaluating configuration changes to aircraft type B suggest that the baseline configuration (200-24) is the most adequate for the legs operated by such aircraft. As illustrated in Figure 4-4, the aggregated revenue of the twelve legs operated with this aircraft type decreases in almost all the alternative arrangements proposed (with the sole exception of the 216-18 configuration in the high demand scenario, where a revenue increase is estimated). Besides, configurations that increase premium cabin capacity hurt AL1’s revenues more than the LOPA’s that add economy cabin seats, mostly at the high demand scenarios. However, as discussed earlier in this chapter, half of the routes operated by aircraft type B in AL1’s network are international and half are domestic; Table 4-8 illustrates how ALLF’s differ between these groups. While economy cabin ALLF’s are considerably high for both cases, the premium cabin ALLF’s are roughly 30 percentage points higher on the international legs. Therefore, different impacts are expected for each of these categories, as confirmed by Figure 4-5: while the 200-24 LOPA is the best suited for the international legs operated by aircraft type B, the model estimates that domestic legs would benefit from replacing premium cabin seats with economy cabin seats (similar to what has been observed for aircraft type A).
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Data-driven revenue management

Data-driven revenue management

In Chapter 4, we prove under certain assumptions on the random component of the demand (e.g., boundedness) that the sample size is related to the accuracy of the [r]

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