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Dissertations / Theses on the topic 'Revenue management'
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1
Shioda, Romy 1977. "Restaurant revenue management." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/28250.
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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2002.Includes bibliographical references (p. 59-60).
We develop two classes of optimization models in order to maximize revenue in a restaurant, while controlling average waiting time as well as perceived fairness, that may violate the first-come-first-serve (FCFS) rule. In the first class of models, we use integer programming, stochastic programming and approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation of a restaurant that does not accept reservations. In a computational study with simulated data, we show that optimization based methods enhance revenle relative to the industry practice of FCFS by 0.11% to 2.22% for low load factors, by 0.16% to 2.96% for medium load factors, and by 7.65% to 13.13% for high load factors, without increasing and occasionally decreasing waiting times compared to FCFS. The second class of models addresses reservations. We propose a two step procedure: use a stochastic gradient algorithm to decide a priori how many reservations to accept for a future time and then use approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation. In a computational study involving real data from an Atlanta restaurant, the reservation model improves revenue relative to FCFS by 3.5% for low load factors and 7.3% for high load factors.
by Romy Shioda.
S.M.
2
Ciocan, Dragos Florin. "High dimensional revenue management." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/108211.
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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, 2014.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 149-153).
We present potential solutions to several problems that arise in making revenue management (RM) practical for online advertising and related modern applications. Principally, RM solutions for these problems must contend with (i) highly volatile demand processes that are hard to forecast, and (ii) massive scale that makes even basic optimization problems challenging. Our solutions to these problems are interesting in their own right in the areas of stochastic optimization, high dimensional learning and distributed optimization. In the first part of the thesis, we propose a model predictive control approach to combat volatile demand. This approach is conceptually simple, uses available demand data in a natural way, and, most importantly, can be shown to generate significant revenue advantages on real-world data from ad networks. Under mild restrictions, we prove that our algorithm achieves uniform relative performance guarantees vis-a-vis a clairvoyant in the face of arbitrary volatility, while simultaneously being optimal in the event that volatility is negligible. This is the first result of its kind for model predictive control. While our approach above is effective at hedging demand shocks that occur over "large" time horizons, it relies on the ability to estimate snapshots of the prevailing demand distribution over "short" time horizons. The second part of the thesis deals with learning the extremely high dimensional demand distributions that are typical in display advertising applications. This work exploits the special structure of the display advertising version of the NRM problem to achieve a sample complexity that scales gracefully in the dimensions of the problem. The third part of the thesis focuses on the problem of solving terabyte sized LPs on an hourly basis given a distributed computational infrastructure; solving these massive LPs is the computational primitive required to make our model predictive control approach practical. Here we design a linear optimization algorithm that fits a paradigm for distributed computation referred to as 'Map-Reduce'. An implementation of our solver in a shared memory environment where we can benchmark against solvers such as CPLEX shows that the algorithm outperforms those solvers on the types of LPs that an ad network would have to solve in practice.
by Dragos Florin Ciocan.
Ph. D.
3
Uichanco, Joline Ann Villaranda. "Data-driven revenue management." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/41728.
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Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2007.Includes bibliographical references (p. 125-127).
In this thesis, we consider the classical newsvendor model and various important extensions. We do not assume that the demand distribution is known, rather the only information available is a set of independent samples drawn from the demand distribution. In particular, the variants of the model we consider are: the classical profit-maximization newsvendor model, the risk-averse newsvendor model and the price-setting newsvendor model. If the explicit demand distribution is known, then the exact solutions to these models can be found either analytically or numerically via simulation methods. However, in most real-life settings, the demand distribution is not available, and usually there is only historical demand data from past periods. Thus, data-driven approaches are appealing in solving these problems. In this thesis, we evaluate the theoretical and empirical performance of nonparametric and parametric approaches for solving the variants of the newsvendor model assuming partial information on the distribution. For the classical profit-maximization newsvendor model and the risk-averse newsvendor model we describe general non-parametric approaches that do not make any prior assumption on the true demand distribution. We extend and significantly improve previous theoretical bounds on the number of samples required to guarantee with high probability that the data-driven approach provides a near-optimal solution. By near-optimal we mean that the approximate solution performs arbitrarily close to the optimal solution that is computed with respect to the true demand distributions.
(cont.) For the price-setting newsvendor problem, we analyze a previously proposed simulation-based approach for a linear-additive demand model, and again derive bounds on the number of samples required to ensure that the simulation-based approach provides a near-optimal solution. We also perform computational experiments to analyze the empirical performance of these data-driven approaches.
by Joline Ann Villaranda Uichanco.
S.M.
4
Githiri, Duncan. "Airline revenue management performance measurement of South African Airways origin-destination revenue management." Thesis, Rhodes University, 2017. http://hdl.handle.net/10962/59188.
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Revenue Management (RM) in the airline industry is the practise of selling fixed capacity as a service over a finite time horizon. The market is characterised by the customers’ willingness to pay different prices for the service. This creates the opportunity to target different customer segments and use price differential to attain the optimal passenger fare mix to maximise revenue. The aim is to maximise revenue and an airline can expect revenue increase of between 3 to 7 percent with the successful implementation of a Revenue Management system. The question of whether the revenue increase can be attributed to the RMS is crucial in determining its level of success and validating the optimisation strategy applied (Rannou and Melli, 2003). South African Airways (SAA) migration from Leg-based optimisation to Origin-Destination (O&D) network based revenue management optimisation created the opportunity for this study to measure and evaluate the RMS performance. Revenue performance measuring tools using inventory systems data to measure RMS performance, ASK (Available Seat Kilometre), RASK (Revenue per Available Seat Kilometre), CASK (Cost per Available Seat Kilometre), RPK (Revenue Passenger Kilometre) and cabin factor yield. The limitations relating to the performance measuring tools utilising inventory system data, is the inability for continuous measurement and the isolation of the impact to revenue due to the RMS on its own. In seeking to gauge the performance of the O&D optimisation, the Revenue Opportunity Model (ROM) is applied. ROM is a post departure measuring tool utilised to continuously measure and isolate the contribution of the RMS on SAA’s O&D network. The revenue opportunity achieved versus the potential revenue was assessed. A revenue comparison of the airlines 2014 and 2015 financial year is performed. The results of the analysis showed the O&D optimisation yielded positive revenue capture on routes that applied the correct optimisation strategy. Recommendations on the optimisation strategy to be applied on routes having average or low revenues captured are presented. The aim is to provide the SAA revenue management department with tangible solutions that would result in increased revenue for the SAA network.
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Zickus, Jeffrey S. (Jeffrey Stuart) 1973. "Forecasting for airline network revenue management : revenue and competitive impacts." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/10103.
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Martens, Tobias von. "Kundenwertorientiertes Revenue-Management im Dienstleistungsbereich." Wiesbaden : Gabler, 2009. http://dx.doi.org/10.1007/978-3-8349-9503-2.
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Defregger, Florian. "Revenue management for manufacturing companies /." kostenfrei, 2009. http://deposit.d-nb.de/cgi-bin/dokserv?idn=997408154.
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Chen, Lijian. "Stochastic programming in revenue management." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1150314352.
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Thraves, Cortés-Monroy Charles Mark. "New applications in Revenue Management." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112085.
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Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references.
Revenue Management (RM) is an area with important advances in theory and practice in the last thirty years. This thesis presents three different new applications in RM with a focus on: the firms' perspective, the government's perspective as a policy maker, and the consumers' perspective (in terms of welfare). In this thesis, we first present a two-part tariff pricing problem faced by a satellite data provider. We estimate unobserved data with parametric density functions in order to generate instances of the problem. We propose a mixed integer programming formulation for pricing. As the problem is hard to solve, we propose heuristics that make use of the MIP formulation together with intrinsic properties of the problem. Furthermore, we contrast this approach with a dynamic programming approach. Both methodologies outperform the current pricing strategy of the satellite provider, even assuming misspecifications in the assumptions made. Subsequently, we study how the government can encourage green technology adoption through a rebate to consumers. We model this setting as a Stackleberg game where firms interact in a price-setting competing newsvendor problem where the government gives a rebate to consumers in the first stage. We show the trade-off between social welfare when the government decides an adoption target instead of a utilitarian objective. Then, we study the impact of competition and demand uncertainty on the three agents involved: firms, government, and consumers. This thesis recognizes the need to measure consumers' welfare for multiple items under demand uncertainty. As a result, this thesis builds on existing theory in order to incorporate demand uncertainty in Consumer Surplus. In many settings, produced quantities might not meet the realized demand at a given market price. This comes as an obstacle in the computation of consumer surplus. To address this, we define the concept of an allocation rule. In addition, we study the impact of uncertainty on consumers for different demand noise (additive and multiplicative) and for various allocation rules.
by Charles Mark Thraves Cortés-Monroy.
Ph. D.
10
Konig, Matthias. "Risk considerations in revenue management." Thesis, Lancaster University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.547943.
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Martens, Tobias von. "Kundenwertorientiertes Revenue-Management im Dienstleistungsbereich." Wiesbaden Gabler, 2008. http://d-nb.info/992494346/04.
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Ďurica, Peter. "Revenue management a jeho využitie v hotelových prevádzkach." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-162376.
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The aim of the thesis is to introduce revenue management as complex managerial approach to hotel's revenue optimization with the case of Accor company. And, based on results of company's Prague's hotels analysis, suggest more effective revenue optimization solutions. To achieve this, the first chapter brings out theoretical knowledge of revenue management, its development, different approaches and tools of revenue optimization. Second chapter brings an insight to Accor company profile. The importance of revenue management for the company and practical cases of revenue optimization tools usage is content of third chapter. The purpose of the last chapter is to identify weaknesses of current revenue management tools application in five hotels of the company in Prague and to suggest steps leading to improved contribution of revenue management tools to revenue optimization. Most of them could bring higher revenue and profit contribution in a short term. Suggested steps are limited by knowledge and experience of the author. They are solely addressed to company's Prague's hotels. Deeper analysis could reveal other weaknesses. To eliminate them, coordinated action of Accor's headquarters is needed. Elimination of these imperfections would lead to improved performance of Accor's Prague's hotels as well.
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Skyba, Stanislav. "Využití renevue managementu k řízení ziskovosti letecké linky." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318134.
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Revenue Management (RM) is a process that is trying to understand, estimate and influence the behaviour of customers to maximize revenues. Constant development in the field of information and communication technology leads to the development of more efficient systems which boost decision-making in the revenue management. RM systems are based on 2 modules, prognostic and optimization. In order for both modules to provide the most accurate estimates and to find effective decisions on booking limits and prices, they need a huge amount of information on demand and other factors. The diploma thesis deals with the theory of income management, description of techniques used in RM, RM systems used in air transport and RM applications on selected airline.
14
Popescu, Andreea. "Air cargo revenue and capacity management." Diss., Available online, Georgia Institute of Technology, 2006, 2006. http://etd.gatech.edu/theses/available/etd-11202006-095545/.
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Thesis (Ph. D.)--Industrial & Systems Engineering, Georgia Institute of Technology, 2007.Dr. Dirk Gunther, Committee Member ; Dr. Hayriye Ayhan, Committee Member ; Dr. Ellis L. Johnson, Committee Chair ; Dr. Pinar Keskinocak, Committee Co-Chair ; Dr. Julie Swann, Committee Member
15
Mohaupt, Michael. "Forschungsansatz zur Unsicherheitsproblematik im Revenue Management." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-70707.
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Die effiziente Nutzung beschränkter Kapazitäten (z.B. Flugzeugplätze, Hotelzimmer) erweist sich für Anbieter als kritischer Erfolgsfaktor. Zur Steuerung der Buchungsanfragen wird daher Revenue Management angewandt. Um langfristig profitable Kundenbeziehungen aufzubauen, sollten auch kundenwertbezogene Informationen (den langfristigen Wert des Kunden für den Anbieter repräsentierend) einbezogen werden. In der Folge sieht sich der Anbieter vielen Unsicherheiten gegenüber. Da die Berücksichtigung von Unsicherheiten die Effizienz der Steuerungsentscheidungen und damit die Erlöshöhe beeinflusst, widmet sich die Dissertation zunächst der Analyse und Systematisierung der unsicherheitsbasierten Problemfelder und nachfolgend der Erweiterung traditioneller Steuerungsmethoden, die in Simulationsstudien evaluiert werden. Die Intention des Beitrags ist es, das Forschungsvorhaben in seiner Zielstellung und Methodologie nachvollziehbar darzulegen.
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Pak, Kevin. "Revenue Management: New Features and Models." [Rotterdam]: Erasmus Research Institute of Management (ERIM), Erasmus University Rotterdam ; Rotterdam : Erasmus University Rotterdam [Host], 2005. http://hdl.handle.net/1765/6771.
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Cooper, William L. "Revenue management, auctions, and perishable inventories." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/25805.
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Wang, Jingbo. "Estimation and optimization in revenue management." Thesis, University of Oxford, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522810.
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Armar, Nii A. "Cargo revenue management for space logistics." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/62971.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 79-82).
This thesis covers the development of a framework for the application of revenue management, specifically capacity control, to space logistics for use in the optimization of mission cargo allocations, which in turn affect duration, infrastructure availability, and forward logistics. Two capacity control algorithms were developed; the first is based on partitioning of Monte Carlo samples while the second is based on bid-pricing with high-frequency price adjustments. The algorithms were implemented in Java as a plugin module to SpaceNet 2.0, an existing integrated modeling and simulation tool for space logistics. The module was tested on a lunar exploration concept which emphasizes global exploration of the Moon using mobile infrastructure. Results suggest that revenue management produces better capacity allocations in shorter duration missions, while producing nominal capacity allocations (i.e. those in the deterministic case) in the long run.
by Nii A. Armar.
S.M.
20
Mak, Chung Yu. "Revenue impacts of airline yield management." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/26838.
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Fry, Daniel G. "Demand driven dispatch and revenue management." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/99548.
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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 149-152).
The focus of this thesis is on the integration of and interplay between demand driven dispatch and revenue management in a competitive airline network environment. Demand driven dispatch is the reassignment of aircraft to flights close to departure to improve operating profitability. Previous studies on demand driven dispatch have not incorporated competition and have typically ignored or significantly simplified revenue management. All simulations in this thesis use the PODS simulator, where stochastic demand by market chooses between competing airlines with alternative paths and fare products whose availability is determined by industry-typical revenue management systems. Demand driven dispatch (D³) is tested with a variety of methods and objectives, including a bookings-based method that assigns the largest aircraft to the flights with the highest forecasted demands. More sophisticated methods include revenue- and profit-maximizing fleet optimizations that directly use the output of leg-based and network-based RM systems and a minimum-cost flow specification. D³ is then tested with a variety of aircraft swap timings, RM systems, and competitive scenarios. Sensitivity testing is performed at a variety of demand levels, demand variability levels, and with an optimized static fleet assignment. Findings include important competitive feedbacks from D³, relationships between D³ and both revenue management and pricing, and important nuances to D³'s relationship with the level and variability of demand. Depending on how it is implemented, D³ may harm competitor airlines more than it aids the implementer. Early swaps in D³ lead to heavy dilution. Late swaps lead to smaller increases in loads but substantial increases in revenue. The relationship between revenue-maximization and cost-minimization in profit-maximizing D³ is highly influenced by the timing of swaps, revenue estimation, and demand levels. Finally, early swaps are susceptible to high variability of demand while late swaps are more robust. Findings indicate that the benefits of D³ can be estimated at operating profit gains of 0.04% to 2.03%, revenue gains of 0.02% to 0.88%, and changes in operating costs of -0.08% to 0.13%.
by Daniel G. Fry.
S.M. in Transportation
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Andersson, Karl, and Henrik Wittgren. "Restaurangbesökarens inställning till Restaurant Revenue Management." Thesis, Örebro universitet, Restaurang- och hotellhögskolan, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-51732.
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Farias, Vivek Francis. "Revenue management beyond "estimate, the optimize" /." May be available electronically:, 2007. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.
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Barocio, Cots Ruben 1970. "Revenue management under demand driven dispatch." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9481.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics; and (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1999.Includes bibliographical references (leaves 129-131).
Demand Driven Dispatch is an operational mode by which airlines can dynamically reassign aircraft types of the same family but of differing capacities to better match capacity with demand in a profit maximizing way. Current algorithms for dynamic reassignment of aircraft types suggest that the nect assignment problem be solved repeatedly during the booking horizon of the pool of flights being considered in the type assignment model. This approach has been shown to produce a 1% to 5% improvement in operating profits when compared to the static fleet assignment currently practiced by airlines. In our work we formalize and explore the necessary interaction between a revenue management model and the fleet assignment decisions that the airline must make under the Demand Driven Dispatch operational mode. Based on our findings. we produce a set of alternate algorithms for dynamic fleet type reassignment which significantly reduces the number of fleet assignment problems that must be solved during the booking horizon of the pool of flights being considered. Using demand data from a major U.S. airline, we simulate both the traditional algorithms used for dynamic fleet reassignment and the alternate algorithms developed in this thesis. Our results show that it is indeed possible to delay the first fleet assignment decision that the airline must make, thus reducing the number of fleet assignment problems that must be solved. Further, we show that our approach can even outperforms the traditional Demand Driven Dispatch algorithms both in terms of revenues and in terms of passenger loads, by integrating the delayed fleet assignment decision with the revenue management process.
by Ruben Barocio Cots.
S.M.
25
Hao, Eric (Eric C. ). "Ancillary revenues in the airline industry : impacts on revenue management and distribution systems." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89854.
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Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2014.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 109-110).
Airlines have increasingly depended on ancillary revenue in response to rising fuel costs, de- creased yields, and an increasingly competitive environment. Estimates indicate that U.S. airlines collected over $8 billion in ancillary revenue in 2012. Ancillary revenue poses challenges for airlines, including revenue management (RM) and distribution since total revenue maximization requires consideration of ancillary revenue and ticket revenue. In this thesis, we: (1) describe trends contributing to the movement towards ancillary revenue; (2) present three methods for incorporating ancillary revenue into revenue management and distribution; (3) evaluate the revenue performance of these methods using the Passenger Origin Destination Simulator (PODS), a competitive airline simulator. One method of including ancillary revenue into RM is RM Input Adjustment with Class Level Estimates, which involves modifying input fares to the optimizer. Because fare values to the optimizer are aggregated by market and class, the airline uses class level estimates of ancillary revenue potential to augment fares. Another method involves modifying the fare value at the time of availability control, or Availability Fare Adjustment. In network optimization, the availability fare refers to the fare used to compare an itinerary-class to the control mechanism, like displacement adjusted virtual nesting (DAVN) or additive bid price (ProBP). Availability Fare Adjustment with Class Level Estimates also involves using class level estimates of ancillary revenue. Alternatively, we test scenarios where the airline estimates ancillary revenue for individual passengers in Customized Availability Fare Adjustment with Passenger Specific Estimates. Although this type of estimation is not feasible yet, results from Customized Availability Adjustment give a theoretical bound to revenue gain. We nd that incorporating ancillary revenue opens availability for lower yield passengers. Revenue increases occur from extra bookings in these classes because more bookings are taken. Revenue losses occur from higher class passengers buying down to cheaper seats. Without willingness to pay (WTP) forecasting, net revenue losses of up to {2.6% are observed. In advanced RM systems with WTP forecasting, revenue gains of +0.6% are observed for Class Level RM Input Adjustment, +0.9% for Class Level Availability Fare Adjustment, and +2.6% for Passenger Specific Customized Availability Adjustment.
by Eric Hao.
S.M. in Transportation
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Veselová, Erika. "Modely sieťového revenue manažmentu." Master's thesis, Vysoká škola ekonomická v Praze, 2012. http://www.nusl.cz/ntk/nusl-165297.
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The progress in information and communication technologies, together with scientific pro-gress in economics, statistics and operational research, influenced the rapid development of process understanding, and estimation and prediction of customer demand, in real-time revenue management. The aim of this work was the detailed description of the essential principles to be included in the business strategy in order to maximize revenue. Network revenue management models seek to maximize revenue when customers buy products with multiple sources. This concept was applied to the example of the aviation industry and a computing program was developed which could be generalized to other industries. This used a "hub-and-spoke" network and programs MS Excel, LINGO and VBA. The paper also describes the basic steps of revenue management that are repeated todetail the fore-casts and dynamically perform re-optimization of the required decisions, to specify the complete process in detail.
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Forsman, Tomas, and Isak Lindstrand. "Restaurant Revenue Management : En studie om hur Revenue Management kan implementeras på restauranger för att öka lönsamhet." Thesis, Örebro universitet, Restaurang- och hotellhögskolan, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-61243.
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Schmidt, Henning. "Simultaneous control of demand and supply in revenue management with flexible capacity." Clausthal-Zellerfeld Papierflieger, 2009. http://d-nb.info/993813461/04.
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Holubec, Jakub. "Využití revenue managementu v ubytovacích zařízeních." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-136279.
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This diploma thesis discusses Revenue management as an operational approach to revenue maximization in the hotel industry. The main objective of this thesis is to assess Revenue management approaches of two Prague hotels based on the comparison with academic theory in the hotel industry. The opening part is focused on prerequisites and tools used in Revenue management. Further, new approaches are introduced as well as current trends that have emerged in the recent time. The closing chapter analyses Revenue management of the selected hotels, including technologies, segmentation, pricing and rate management, demand forecasting, distribution, ancillary revenue streams and more. Finally, each Revenue management approach is subjectively confronted and assessed.
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Barz, Christiane. "Risk-averse capacity control in revenue management." Berlin : Springer, 2007. http://dx.doi.org/10.1007/978-3-540-73014-9.
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Wong, Sau-lim Tim. "Airline revenue management passenger right and protection /." Click to view the E-thesis via HKUTO, 2005. http://sunzi.lib.hku.hk/hkuto/record/B31633183.
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Terciyanli, Erman. "Alternative Mathematical Models For Revenue Management Problems." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610711/index.pdf.
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In this study, the seat inventory control problem is considered for airline networks from the
perspective of a risk-averse decision maker. In the revenue management literature, it is generally
assumed that the decision makers are risk-neutral. Therefore, the expected revenue is
maximized without taking the variability or any other risk factor into account. On the other
hand, risk-sensitive approach provides us with more information about the behavior of the
revenue. The risk measure we consider in this study is the probability that revenue is less
than a predetermined threshold level. In the risk-neutral cases, while the expected revenue
is maximized, the probability of revenue being less than such a predetermined level might
be high. We propose three mathematical models to incorporate the risk measure under consideration.
The optimal allocations obtained by these models are numerically evaluated in
simulation studies for example problems. Expected revenue, coefficient of variation, load factor
and probability of the poor performance are the performance measures in the simulation
studies. According to the results of these simulations, it shown that the proposed models can
decrease the variability of the revenue considerably. In other words, the probability of revenue
being less than the threshold level is decreased. Moreover, expected revenue can be increased
in some scenarios by using the proposed models. The approach considered in this thesis is especially proposed for small scale airlines because risk of obtaining revenue less than the
threshold level is more for this type of airlines as compared to large scale airlines.
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Eroglu, Fatma Esra. "Service Models For Airline Revenue Management Problems." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613490/index.pdf.
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In this thesis, the seat inventory control problem is studied for airlines from the perspective
of a risk-averse decision maker. There are only a few studies in the revenue
management literature that consider the risk factor. Most of the studies aim at finding
the optimal seat allocations while maximizing the expected revenue and do not take
the variability of the revenue and hence a risk measure into account. This study aims
to decrease the variance of the revenue by increasing the capacity utilization called
load factor in the revenue management literature. In addition to expected revenue,
load factor is an important performance measure the state companies work with. For
this purpose, two types of models with load factor formulations are proposed. This
thesis is the first study in the revenue management literature for the airline industry
that uses the load factor formulations in the mathematical models. It is an advantage
to work with load factor formulations since the models with load factor formulations
are much easier to formulate and solve as compared to other risk sensitive models in
the literature. The results of the proposed models are evaluated by using simulation
for a sample network under different scenarios. The models we propose allow us to control the variability of revenue by changing the used capacity of the aircraft. This
is at the expense of a decrease in the revenue under some scenarios. The models we
propose perform satisfactorily under all scenarios and they are strongly recommended
to be used especially for the small-scale airline companies and state companies and
for scheduling new flights even in large scale, well established airline companies.
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Chahar, Kiran. "Revenue and order management under demand uncertainty." Connect to this title online, 2008. http://etd.lib.clemson.edu/documents/1219855173/.
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Fernandes, A. T. "Spectrum management for revenue maximisation in DSL." Thesis, University of Cambridge, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598991.
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Digital Subscriber Line technologies use existing telephone cables to provide high speed data communications. Spectrum Management in DSL is a set of policies to contain the damage caused by interference between modems. Current practice in Spectrum Management controls this damage by placing constraints on power and spectrum. But recent advances in research define the constraints directly in terms of data loss. The spectrum adopted by interferers is allowed to be much more flexible, as long as the data rate targets are respected. These technologies are collectively called Dynamic Spectrum Management and have achieved huge gains in achievable data rates ([Yu (2002)] [Cendrillon et, al. (2003)]). The enforcement of data rate targets is done via discovering the set of feasible sets of data rates, the data rate region. But there remains a-problem left open on how to decide between alternative feasible sets of data rates. The algorithms in DSM typically find the best spectrum for a new user, given that pre-existing users have already been allocated data rate targets. The question of how to allocate these data: rate targets over time has been left unanswered. An additional complication is that the DSL channel is a MIMO channel that is only partially known, and gradually discovered. The crosstalk functions between different users and the direct channels are only discovered as new users request DSL, and this occurs gradually over time. This thesis introduces an economic focus to solve both problems. Among alternative, feasible set of data rates, the one with the best revenue potential is chosen. And the uncertainty about the MIMO channel and user take-up is included in a estimate of expected revenue. This is the first main contribution of this thesis, Economic Spectrum Management. The second contribution is a new, fast and near-optimal spectrum allocation algorithm, the Noise Floor Mask (NFM) algorithm. It has been developed as a faster alternative to the algorithm that achieves optimal data rates, Optimum Spectrum Management (OSM). OSM involves a more computationally expensive function optimisation and may present exponential complexity on the number of users, under certain conditions. Even for small number of users, NFM is fast and light, and thus useful for conducting Monte Carlo projections of the data rate region, given statistics on the telephone cable. These projections axe necessary in Economic Spectrum Management to compute expected revenues, given that the MIMO channel is not fully known. The NFM algorithm is based on a common insight among engineers about the "noise floor" as a relevant milestone when assessing how damaging a particular source of interference is. The rule of thumb is that interference sources less powerful than the background noise are significantly less relevant than those "above the noise floor". This intuition was developed and quantified, to form the basis of NFM.
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Strauss, Arne Karsten. "Optimisation in choice-based network revenue management." Thesis, Lancaster University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.543995.
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王守廉 and Sau-lim Tim Wong. "Airline revenue management: passenger right and protection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B31633183.
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Charania, Aamer 1970. "Incorporating sell-up in airline revenue management." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/10105.
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Cusano, Andrew Jacob 1978. "Airline revenue management under alternative fare structures." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/26900.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.Includes bibliographical references (leaves 119-120).
Airline revenue maximization consists of two main components: pricing and revenue management. Revenue management systems are used to control seat inventory given a forecasted demand to maximize revenues. Fare structures have been constructed by major network airlines to segment demand with multiple fare products and numerous restrictions, a practice known as differential pricing. The increasing presence of low-cost carriers with simplified fare structures (compressed fare levels and fewer booking restrictions) combined with recent market demand shifts have led some major network carriers to explore the use of simplified fare structures. This research examines the performance of revenue management systems under these alternative fare structures as compared to the performance of these systems with the traditional fare structure. The objective is to measure the impacts on overall revenue and revenue management under alternative fare structures. The Passenger Origin-Destination Simulator (PODS) is used in this research to test the impact on revenue management of alternative fare structures. Results show that alternative fare structures lead to overall revenue reductions. The magnitude of reduction is as high as 20 percent when all fare restrictions are removed compared to the traditional base case fare structure. However, leg-based fare-class revenue management still produces a large revenue gain, up to 17 percent, over a first-come-first-serve regime regardless of the fare structure used. Furthermore, incremental revenue gains from origin-destination control as opposed to fare-class revenue management are still present with alternative fare structures. The incremental revenue gains are greater than 1 percent in all cases and greater than 3 percent when advance purchase requirements are removed. In the case when all restrictions are removed, origin-destination control actually performs better at a given network average load factor than with a traditional fare structure.
by Andrew Jacob Cusano.
S.M.
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Boer, Sanne Vincent de 1976. "Advances in airline revenue management and pricing." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/16926.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2003.Includes bibliographical references (p. 160-168).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
We propose new models and optimization methods for airline revenue management and pricing. In the first part of this thesis, we study the dynamic inventory control problem for a single flight under imperfect market segmentation, when customers book the lowest available class whose restrictions they can satisfy and whose fare they are willing to pay. We derive theoretical properties of the value functions and optimal policy for a generic single-resource revenue management problem, of which this problem is a special case. Numerical examples show that adjusting the booking policy for imperfections of the market segmentation leads to significant revenue gains. In the second part, we study the impact of dynamic capacity management on airline seat inventory control. Through better matching the supply and demand for seats the airline is able to carry more passengers, and the revenue management policy should be adjusted accordingly. We propose a derivative of the widely used EMSRb booking limit calculation method that takes into account the effect of future capacity changes, which can lead to significant revenue gains. In the third part, we propose a simulation-based optimization approach for seat inventory control in a network environment. Starting with any nested booking-limit policy, we combine a stochastic gradient algorithm and approximate dynamic programming ideas to improve the initial booking limits. Numerical experiments suggest that the proposed algorithm can lead to practically significant revenue enhancements. In the fourth part, -we study a joint pricing and resource allocation probleml in a network with applications to production planning and airline revenue management. We show that the objective function reduces to a convex optimization problem for certain types of demand distributions, which is tractable for large instances.
(cont.) We propose several approaches for dynamic picing and resource allocation. Numerical experiments suggest that coordination of pricing and resource allocation policies in a network while taking into account the uncertainty of demand can lead to significant revenue gains. Finally, in our conclusions we propose an integrated approach to airline revenue management that combines all four aspects that we studied here, and suggest directions for future research.
by Sanne Vincent de Boer.
Ph.D.
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Bratu, Stephane (Stephane J.-C) 1970. "Network value concept in airline revenue management." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9939.
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D'Huart, Olivier (Olivier Edouard Marie). "A competitive approach to airline revenue management." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60708.
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Thesis (S.M. in Transportation)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis. Page 140 blank.
Includes bibliographical references (p. 137-139).
Since the 1980s, the airline industry has seen two major changes: Deregulation, which led to an increase in competition, and the development of revenue management systems. Paradoxically the revenue management models used have not incorporated many competitive considerations. In this thesis we study the interactions between existing airline revenue management systems in competitive markets. We use the Passenger Origin Destination Simulator (PODS) for simulations. After a review of the past research on such interactions, we develop our own model and use simulations to identify and measure the extent to which revenue management systems of competing airlines affect each other. The model introduced highlights the importance of spill-of-demand between airlines. We show that with current revenue management practice, a legacy carrier should be less sensitive than a low-cost carrier to revenue management competitive interactions. As compared to an equivalent monopoly, an airline oligopoly tends to allocate more seats to high-fare passengers and fewer seats to low-fare passengers. With steady demand distributions, an airline's expected revenues are a decreasing function of the seat capacity allocated by its competitors to high-fare passengers. Existing revenue management systems react to competitor moves automatically only if a change in the seat allocation rule by an airline occurs over a large enough number of successive departures to be detected by forecasters. We then suggest how to improve revenue management based on the interactions identified. With steady demands, if a competitor increases (respectively decreases) its seat allocation for high-fare passengers, the best response to optimize revenues on the short-term is to decrease (respectively increase) seat allocation for high-fare passengers. We also show that the use of EMSRb-optimization by competitors results in a near-optimum competitive equilibrium, and a near-optimum cooperative equilibrium if airlines do not share revenues. Rarely can competitive interactions justify an airline to override the EMSRb seat-allocation rule to optimize revenues. Last, we introduce LOCO-based Forecast Multiplication, a heuristic forecast adjustment made in response to the current seat availability of the competitors that can increase an airline's revenues substantially.
by Olivier d'Huart.
S.M.in Transportation
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Liu, Tieming Ph D. Massachusetts Institute of Technology. "Revenue management models in the manufacturing industry." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33736.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 107-110).
In recent years, many manufacturing companies have started exploring innovative revenue management technologies in an effort to improve their operations and ultimately their bottom lines. Methods such as differentiating customers based on their sensitivity to price and delays are employed by firms to increase their profits. These developments call for models that have the potential to radically improve supply chain efficiencies in much the same way that revenue management has changed the airline industry. In this dissertation, we study revenue management models where customers can be separated into different classes depending on their sensitivity to price, lead time, and service. Specifically, we focus on identifying effective models to coordinate production, inventory and admission controls in face of multiple classes of demand and time- varying parameters. We start with a single-class-customer problem with both backlogged and discretionary sales. Demand may be fulfilled no later than N periods with price discounts if the inventory is not available. If profitable, demand may be rejected even if the inventory is still available.
(cont.) For this problem we analyze the structure of the optimal policy and show that it is characterized by three state-independent control parameters: the produce-up-to level (S), the reserve-up-to level (R), and the backlog-up-to level (B). At the beginning of each period, the manufacturer will produce to bring the inventory level up to S or to the maximum capacity; during the period, s/he will set aside R units of inventory for the next period, and satisfy some customers with the remaining inventory, if expected future profit is higher; otherwise, s/he will satisfy customers with the inventory and backlog up to B units of demands. Then, we analyze a single-product, two-class-customer model in which demanding (high priority) customers would like to receive products immediately, while other customers are willing to wait in order to pay lower prices. For this model, we provide a heuristic policy characterized by three threshold levels: S, R, B.
(cont.) In this policy, during each period, the manufacturer will set aside R units of inventory for the next period, and satisfy some high priority customers with the remaining inventory, if expected future profit is higher; otherwise, s/he will satisfy as many of the high priority customers as possible and backlog up to B units of lower priority customers. Finally, we examine production, rationing, and admission control policies in manufacturing systems with both make-to-stock(MTS) and make-to-order(MTO) products. Two models are analyzed. In the first model, which is motivated by the automobile industry, the make-to-stock product has higher priority than the make-to-order product. In the second model, which is motivated by the PC industry, the manufacturer gives higher priority to the make-to-order product over the make-to-stock product. We characterize the optimal production and order admission policies with linear threshold levels. We also extend those results to problems where low-priority backorders can be canceled by the manufacturer, as well as to problems with multiple types of make-to-order products.
by Tieming Liu.
Ph.D.
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Remy, Detlev. "Revenue management in for-profit higher education." Thesis, University of Surrey, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665501.
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Higher education is influenced by social, cultural, economic and academic drivers (Knight, 2004). According to Marginson (2003) education is moving in the direction of marketization and is also becoming more profit-driven. Researching for-profit higher education, Fried and Hill (2009:37) state that "higher education is different from most goods in several ways". Nonetheless, for-profit higher education has to maintain its profitability to stay not only in a very competitive market but one in which external factors have a huge impact. One way to react to the changing landscape could be the application of revenue management principles to for-profit higher education. Revenue Management is nowadays of growing importance across several industries which face capacity constraints and fluctuations in demand. Hence, the purpose of this thesis is to examine how revenue management can be applied in for-profit higher education. Based on a realism research paradigm, the author has conducted a single case study design with embedded units, · by interviewing 19 members of management in a leading for-profit hospitality school which offers higher education programs. The aim of the research was two-fold; first, to analyse the specifics of for-profit higher education, and second, to investigate what implications this has for the management of pricing and capacity. This has led to the following findings: Although revenue management is applied nowadays in many industries it is necess·ary in order to fully understand the practice, to classify the type and application of revenue management practice in terms of preconditions and components and tools of revenue management utilised, For-profit higher education looks like a suitable candidate for revenue management practice, however only a 'loose' revenue management model can be applied, mainly based on the use of scholarships and optimised capacity utilisation,
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Riedel, Silvia. "Forecast combination in revenue management demand forecasting." Thesis, Bournemouth University, 2008. http://eprints.bournemouth.ac.uk/9640/.
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The domain of multi level forecast combination is a challenging new domain containing a large potential for forecast improvements. This thesis presents a theoretical and experimental analysis of different types of forecast diversification on forecast error covariances and resulting combined forecast quality. Three types of diversification are used: (a) diversification concerning the level of learning (b) diversification of predefined parameter values and (c) the use of different forecast models. The diversification is carried out on forecasts of seasonal factor predictions in Revenue Management for Airlines. After decomposing the data and generating diversified forecasts a (multi step) combination procedure is applied. We provide theoretical evidence of why and under which conditions multi step multi level forecast combination can be a powerful approach in order to build a high quality and adaptive forecast system. We theoretically and experimentally compare models differing with respect to the used decomposition, diversification as well as the applied combination models and structures. After an introduction into the application of forecasting seasonal behaviour in Revenue Management, a literature review of the theory of forecast combination is provided. In order to get a clearer idea of under which condition combination works, we then investigate aspects of forecast diversity and forecast diversification. The diversity of forecast errors in terms of error covariances can be expressed in a decomposed manner in relation to different independent error components. This type of decomposed analysis has the advantage that it allows conclusions concerning the potential of the diversified forecasts for future combination. We carry out such an analysis of effects of different types of diversification on error components corresponding to the bias-variance-Bayes decomposition proposed by James and Hastie. Different approaches of how to include information from different levels into forecasting are also discussed in the thesis. The improvements achieved with multi level forecast combination prove that theoretical analysis is extremely important in this relatively new field. The bias-variance-Bayes decomposition is extended to the multi level case. An analysis of the effects of including forecasts with parameters learned at different levels on the bias and variance error components show that forecast combination is the best choice in comparison to some other discussed alternatives. The proposed approach represents a completely automatic procedure. It realises changes in the error components which are not only advantageous at the low level, but have also a stabilising effect on aggregates of low level forecasts to the higher level. We also identify cases in which multi level forecast combination should ideally be connected with the use of different function spaces and/or thick modelling related to certain parameter values or preprocessing procedures. In order to avoid problems occurring for large sets of highly correlated forecasts when considering covariance information, we investigated the potential of pooling and trimming for our case. We estimate the expected behaviour of our diversified forecasts in purely error variance based pooling represented by a common approach of Aiolfi and Timmermann and analyse effects of different kinds of covariances on the accuracy of the combined forecast. We show that a significant loss in the expected forecast accuracy may ensue because of typical inhomogeneities in the covariance matrix for the analysed case. If covariance information is available in a sufficiently high quality, it is possible to run a clustering directly based on covariance information. We discuss how to carry out a clustering in that case. We also consider a case (quite common in our application) when covariance information may not be available and propose a novel simplified representation of the covariance matrix which represents the distance in the forecast generation space and is only based on knowledge about the forecast generation process. A new pooling approach is proposed that avoids inhomogeneities in the covariance matrix by considering the information contained in the simplified covariance representation. One of the main advantages of the proposed approach is that the covariance matrix does not have to be calculated. We compared the results of our approach with the approach of Aiolfi and Timmermann and explained the reasons for significant improvement. Another advantage of our approach is that it leads to the generation of novel multi step, multi level forecast generation structures that carry out the combination in different steps of pooling. Finally, we describe different evolutionary approaches in order to generate combination structures automatically. We investigate very flexible approaches as well as approaches that avoid the expected inhomogeneities in the error covariance matrix based on our theoretical findings. The theoretical analysis is supported by experimental results. We could achieve an improvement of forecast quality up to 11 percent for the practical application of demand forecasting in Revenue Management compared to the current optimised forecasting system.
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Yousef-Sibdari, Soheil. "Essays in Revenue Management and Dynamic Pricing." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/27127.
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In this dissertation, I study two topics in the context of revenue management. The First topic involves building a mathematical model to analyze the competition between many retailers who can change the price of their respective products in real time. I develop a game-theoretic model for the dynamic price competition where each retailer's objective is to maximize its own expected total revenue. I use the Nash equilibrium to predict market equilibrium and provide managerial insights into how each retailer should take into account its competitors' behavior when setting the price.
The second topic involves working with Amtrak, the national railroad passenger corporation, to develop a revenue management model. The revenue management department of Amtrak provides the sales data of Auto Train, a service of Amtrak that allows passengers to bring their vehicles on the train. I analyze the demand structure from sales data and build a mathematical model to describe the sales process for Auto Train. I further develop an algorithm to calculate the optimal pricing strategy that yields the maximum revenue. Because of the distinctive service provided by Auto Train, my findings make important contribution to the revenue management literature.Ph. D.
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Bodea, Tudor Dan. "Choice-based revenue management a hotel perspective /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24739.
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Thesis (Ph.D.)--Civil and Environmental Engineering, Georgia Institute of Technology, 2008.Committee Chair: Garrow, Laurie Anne; Committee Member: Castillo, Marco; Committee Member: Ferguson, Mark; Committee Member: McCarthy, Patrick; Committee Member: Meyer, Michael.
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Joshi, Kapil. "Modeling alternate strategies for airline revenue management." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000557.
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Becher, Michael. "Integrated capacity and price control in Revenue Management a fuzzy system approach." Wiesbaden Gabler, 2007. http://d-nb.info/986403490/04.
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Schröder, Anika. "Dynamic Pricing für parallele Flüge /." Clausthal-Zellerfeld : Papierflieger, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=016710472&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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