Loss systems are of great importance for price optimization and revenue management even after more than a century since their first appearance. In this thesis, we analyze the optimal pricing problem for an M/M/1/1 Erlang-loss systems, and apply the model to inspect the impacts of vacancy tax regulations on short-term rental hosts. We then work on M/M/N/N loss systems while considering both advance reservation and multinomial logit (MNL) choice-model for the customers. We develop a simulation for this system and then train a machine learning (ML) model based on the outputs of this simulation to predict the utilization of each server based on different queueing parameters. Finally, we train another ML model for price optimization when the decision-maker sets the price for all servers to maximize the revenue of the whole system. We show that the presence of advance reservation decreases the utilization, consequently reducing the profit in the corresponding system. / Thesis / Master of Science (MSc)

Diploma Thesis deals with costing and preparation of bids in the transport department of Logistics Company. Analyzed company annually participates in a large numbers of tenders for logistics provider. The aim of diploma thesis is to find critical points and propose a possible method of calculations, which has been missing. Practical section is split into two parts. In first part is using modified calculation formula calculated cost per kilometer. Work continues with two case studies, where are described the most problematic issues and suggested solutions. In the end of work is proposed new methodology of preparing commercial studies which can company use in future.

Esta investigación se centró en el análisis de los modelos y metodologías para la fijación de precios en habitaciones de hoteles con la finalidad de establecer la metodología adecuada dependiendo si el hotel es de cadena o independiente.  Se desarrolla con la finalidad de presentar las metodologías para establecer precios de habitaciones que puedan ser usadas, tanto para fines académicos como en el ejercicio profesional de los hoteleros. Se utilizó la metodología de revisión bibliográfica, partiendo del análisis de publicaciones académicas acerca de metodologías/modelos utilizados en hoteles para la fijación de precios de habitaciones, preferentemente generadas dentro del periodo 2013 hasta el 2020. Se usó como base investigaciones previas sobre la aplicación del revenue management en hoteles, los cuales tienen en cuenta que el precio es uno de los factores más importantes en la decisión de compra de los consumidores. Como resultado se obtuvo que el revenue management se dirige a hoteles de cadena, mientras que el modelo de Hubbart es preferible en hoteles de una categoría menor enfocados en la competencia por costos. Por último, el modelo hedónico puede ir dirigido a cualquiera de los establecimientos, ya sean estos independientes que no tienen acceso a un software más sofisticado, u hoteles parte de una cadena. / This research focused on the analysis of models and methodologies for price setting in hotel rooms in order to establish the appropriate methodology depending if the hotel is part of a chain or independent. It is developed with the purpose of presenting methodologies to establish room prices that can be used for academic and professional purposes of hoteliers. The methodology for the bibliographic review was based on the analysis of academic papers about methodologies and models used in hotels for setting room prices, preferably within the period 2013 to 2020. Previous research on the application of revenue management in hotels was used as a basis, which establish that price is one of the most important factors in the purchase decision of consumers. As a result, it was found that revenue management is aimed at chain hotels, while the Hubbart model is more suitable for hotels of a lower category focused on price competition. Finally, the hedonic model can be used by all the establishments, these being independent with no access to sophisticated software, or even hotel chains. / Trabajo de investigación

Accurately representing the price-demand relationship is critical for the success of a price optimization system. This research first uses booking data from 28 U.S. hotels to investigate the validity of two key assumptions in hotel revenue management. The assumptions are: 1) customers who book later are willing to pay higher rates than customers who book earlier; and, 2) demand is stronger during the week than on the weekend. Empirical results based on an analysis of booking curves, average paid rates, and occupancy rates for group, restricted retail, unrestricted retail, and negotiated demand segments challenge the validity of these assumptions. The combination of lower utilization rates and greater product differentiation suggests that hotels should apply different approaches than simply matching competitor rates to avoid losing market share. On days when inventory is near capacity, traditional yield management tactics deliver tremendous value, but these should be augmented by incorporating price response of demand and competition effects. On days when demand is soft and occupancy is projected to be low, price and competition based strategies should dominate. The hotel price optimization problem with linear demand model is a quadratic programming problem with prices of products that utilize multiple staynight rooms as the decision variable. The optimal solution of the hotel price optimization problems has unique properties that enables us to develop an alternative optimization algorithm that does not require solving quadratic optimization problem. Using the well known least norm problem as a subroutine, the optimization problem can be solved as finding a minimum distance between a polyhedron defined by non-negative demand and capacity constraints. This algorithm is efficient when only a few of the staynights are highly constrained. In practice, the choice of a demand model is largely driven by the ease of estimation and model fit statistics such as R2 and mean absolute percentage error (MAPE). These metrics provide measures of statistical validity of the model, however, they do not measure how well the price optimization will perform which is the ultimate interest of the practitioners. In order to measure the impact of demand models on price optimization performance, we first investigate the goodness of fit of linear demand models with different driver variables using actual data from 23 U.S. hotels representing multiple brands and location types. We find that hotels within the same location types (such as urban, suburban, airport) share similar driver variables. Airport and suburban hotels have simpler model specifications with less drivers compared to the urban hotels. The airport hotel demand models are different from other location hotels in that the airport hotel demand level does not differ by day of week. We then measure the impact of demand model misrepresentation on the performance of price optimization through simulation experiments, which are performed for different levels of demand and forecast accuracy to represent various market environments that hotels operate in. We find that using models with missing driver variables can reduce the potential revenue by 13%∼53% and using the wrong functional form 5%∼43% under our simulation environment. The findings from our research imply that correctly representing the demand model in price optimization is crucial to its success. In order for hotels to realize the maximum potential revenue through pricing, efforts should be focused on identifying the major driver variables influencing demand including the ones that we found to be significant.

The aim of this thesis is to assess modes of transport and to choose the correct one with the possibility of compressing waste. Depending on key parameters which influence the final price, the most economical option is chosen. These variants are prepared for the following application in the software Neruda. It is necessary to evaluate the system of collection as a whole and to choose realistic and economical transport distances with transfer stations available.

This thesis examines how recent advances in debaised machine learning can be used for estimating price elasticities of demand within the automotive insurance field. Traditional methods such as generalized linear model (GLM) to estimate demand has no way of ensuring there are no biases in the underlying data selection, especially when the confounding variables are many. These approaches instead rely on the user’s experience to remove biases in the data. Advances, in the crossing fields between economics and machine learning have however found new approaches to debias datasets automatically through the double machine learning approach (DML). Using a large data set of insurance offers and sales from a Swedish insurance company, the double machine learning approach first described by Chernozhukov et al. (2016) is used to estimate the price elasticity of demand for individual customers. The price elasticities are then grouped on variables of importance and combined with the loss ratio of the segment in order to optimize the existing insurance tariff. In terms of model performance, the importance of the first stage classifier and regressor proved to be important for the final results. In alignment with expectations of the results, higher premium cars such as BMW and Mercedes proved to be more price sensitive. However, these brands also had higher loss ratios which resulted in a lower potential for lowering prices. Aligning the price elasticity with the loss ratios and the company’s strategy was found to be an important aspect. On average, the automotive insurance industry was shown to be price sensitive with few segments of inelastic characteristics. / I denna rapport undersöks hur de senaste framstegen inom maskininlärning kan användas för att uppskatta priselasticiteten hos efterfrågan inom bilförsäkringar. Traditionella metoder som generalized linear model (GLM) för att uppskatta efterfrågan har inget sätt att säkerställa att det inte finns några biaser i det underliggande dataurvalet, särskilt inte när det finns många variabler som kan påverka. Dessa metoder förlitar sig i stället på användarens erfarenhet för att ta bort bias i variablerna. Inom området där ekonomi och maskininlärning korsas har man däremot funnit nya metoder för att automatiskt ta bort bias i dataset genom dubbel maskininlärning (DML). Med hjälp av en stor datamängd av försäkringsofferter och försäljningsresultat från ett svenskt försäkringsbolag används den dubbla maskininlärningsmetoden som först beskrevs av Chernozhukov et al. (2016) för att uppskatta priselasticiteten hos individuella kunder. Priselasticiteterna grupperas sedan på variabler av intresse och kombineras med segmentets skadefrekvens för att optimera den befintliga försäkringstariffen. När det gäller modellens prestanda visade sig betydelsen av det första stegets klassificerare och regressorer vara viktiga för slutresultaten. I linje med förväntningarna på slutresultatet visade sig bilar med högre premier, som BMW och Mercedes, vara mer priskänsliga. Dessa märken hade dock också högre skadefrekvenser, vilket resulterade i en lägre potential för prissänkningar. En viktig slutsats från resultat var vikten av att justera och anpassa prisoptimeringen efter segmentets skadefrekvens och företagets strategi. I genomsnitt visade sig bilförsäkringsbranschen vara priskänslig med få segment med oelastiska egenskaper.

In this thesis, we analyze the impact of the decentralization of price and leadtime decisions made by the marketing and production departments, respectively, in a make-to-order firm. We first study a monopoly environment, and find that in the decentralized setting, the total demand generated is larger, leadtimes are longer, quoted prices are lower, and the firm profits are lower as compared to the centralized setting. We show that coordination can be achieved using a transfer price contract with bonus payments, where both departments receive a fraction of the total revenues generated as a bonus payment. In the second study, we extend this work to a duopoly environment, where two firms compete on the basis of their price and leadtime quotes in a common market. We find that under intense price competition, firms may suffer from a decentralized structure, particularly under high flexibility induced by high capacity, where revenue based sales incentives motivate sales/marketing for more aggressive price cuts resulting in eroding margins. We take the parameters of the demand models in the first two studies as constant, while estimating those parameters based on historical data is a very important problem in practice. In the last study of this thesis, we address the challenges encountered in estimating the price sensitivity of customers shifting focus to the passenger travel industry. We explore how to obtain better price elasticity estimates through an empirical study with an emphasis on the endogeneity problem, which arises as a result of the simultaneous determination of supply and demand. We show that if one does not account for endogeneity, price elasticities may induce an upward-sloping demand curve suggesting that high price produces high demand, or may be biased downward to the extent that elastic demand curves are incorrectly classified as inelastic. We show the improvement in price elasticities through an instrumental variable approach.

This diploma thesis deals with the cost analysis and cost optimization of an exact project of a wooden house in a construction company. The thesis is divided into two parts. The first part is the research part, where basic concepts are defined. The second part of this thesis is the practical part which includes a brief description of the concerned wooden house with its preliminary and final budget. Individual parts of these budgets are subject to the cost analysis. As a result of the cost analysis the price differences between the budgets are shown. In the next step the verification bill of quantities with an item based budget in the ÚRS price system is put together. It is used for comparison and determination of price differences. According to the obtained data, optimization of the preliminary budget is proposed. At the end of this thesis an overall evaluation of price differences and proposed measures are described.

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