This thesis presents a dynamic pricing model where a seller offers two types of a generic product to a random number of customers. Customers show up sequentially. When a customer arrives, he will ---depending on the prices---either purchase one unit of type 1 product or one unit of type 2 product, or will leave empty-handed. The sale ends either when the entire stock is sold out, or when the customers are exhausted. The seller's task is to post the optimal prices for the two product types to each customer to maximize the expected total revenue. We use dynamic programming to formulate this problem, and derive the optimal policy for special cases. For general cases, we develop an algorithm to approximate the optimal policy and use numerical examples to demonstrate the efficiency of the algorithm. Finally, we apply the results to a continuous-time model where customers arrive according to a Poisson process. We develop a heuristic policy and use numerical examples to show the heuristic policy is very effective. / Master of Science

Tillämpningen av dynamisk prissättning har förenklats i och med digitaliseringens framväxt. Med den ökade tillgången till kunddata kan företag idag kartlägga kundernas köpbeteende och genom algoritmer anpassa priserna därefter. Identiska varor och tjänster kan på så sätt prissättas annorlunda. För företaget utgör denna prissättning en möjlighet till ökad lönsamhet, men ur ett kundperspektiv kan detta tänkas skapa känslor av orättvisa och lurendrejeri. Samtidigt har det visat sig vara desto viktigare i en digital miljö att se till kundnöjdhet, eftersom det finns en högre transparens och kunderna har lägre engagemang och lojalitet. Det kan därför ifrågasättas om ett företag i längden tjänar på att använda sig av dynamisk prissättning om den skadar relationen till kunden. Syftet med denna studie är således att undersöka kundattityd kopplat till dynamisk prissättning. För att undersöka kundattityd baseras studien på en modell med komponenterna kundvärde, kundnöjdhet, kvalitet, kundlojalitet, förtroende och rättvisa. Studiens analys grundar sig på empirisk data som samlats in genom virtuella fokusgruppsdiskussioner. Dataunderlaget utgörs av fem fokusgruppsdiskussioner, med totalt 31 deltagare. Studien visar att kundernas attityd till företaget påverkas negativt av dynamisk prissättning. Dock framhäver studien att kundvärdet skapas i andra kvaliteter än pris. Trots en initial negativ inställning påvisas att det finns en viss grad av kundanpassning och utveckling av köpstrategier.

Os consumidores residenciais de energia elétrica no Brasil pagam um preço constante pela mesma em qualquer horário do dia, a despeito da variação constante nos custos de oferta. Isto não é economicamente eficiente. Para se atingir esta eficiência a implantação de uma tarifa inteligente se faz necessária, questão mais factível com o advento das redes inteligentes. Este trabalho busca antever se este desenvolvimento é custo efetivo ou não. Em primeiro lugar, os conceitos de redes inteligentes e de medidores avançados são apresentados. Em segundo lugar, são apresentados os conceitos de resposta da demanda e se demonstra porque o preço da eletricidade, para o consumidor final, deve ser maior na ponta do que fora da ponta. Por fim, se busca fazer uma análise custo benefício de um projeto hipotético de Infraestrutura de Leitura Avançada, desenvolvido por uma distribuidora de energia da região Centro Oeste do Brasil, a partir do estudo de cenários. Esse projeto hipotético ocorre num horizonte de dez anos, entre 2014 e 2023. O primeiro passo foi o desenvolvimento de campanhas de medição entre os anos de 2012 e 2013. Usando os dados aí obtidos, duas curvas de carga horárias foram desenvolvidas, uma para os dias úteis e a outra para finais de semana e feriados. O horário de pico é entre as 19 e as 22 horas nos dias úteis, e das 18 as 23 horas nos finais de semana e feriados. O custo da oferta e o consumo total de eletricidade foram obtidos, respectivamente, no Operador Nacional do Sistema e na Agência Nacional de Energia Elétrica. Os resultados obtidos em 15 experimentos prévios foram usados para estimar as hipotéticas elasticidades preço e elasticidades de substituição. Duas modalidades tarifárias foram testadas nos cenários: Tarifa Pelo Horário de Uso e Tarifa Pelo Horário de Uso com Preço de Pico Crítico. Os resultados obtidos ficaram aquém dos conceitualmente previstos. Uma análise é feita para tentar entender a razão desta resposta. / Residential customers in Brazil pay a constant price throughout the day, despite the large time variation in costs of supply. It is not economically efficient. It is necessary to set it to costumers with smart rates, and this possibility is getting closer from the development of smart grids. This work aims understand in advance if this deployment is cost-effective or not. Firstly, the concepts of Smart Grids, AMR (Automatic Meter Reading) and AMI (Advanced Metering Infrastructure) are presented. Secondly, concepts of demand response are described, and there is a demonstration of the reasons why electricity peak prices must be higher than off-peak prices. Thirdly, we seek to make a cost-benefit analysis for a hypothetical AMI project installation to residential customers, served by a utility in the Middle West of Brazil, under some potential scenarios. This hypothetical project runs in a ten year horizon (2014-2023). The first step was to perform measurement campaigns in 2012 and 2013. Using the data obtained, two residential hourly load curves were developed, one for weekdays and another for weekends and holidays. Peak time occurs between 7 and 10 PM in weekdays, and from 6 to 11 PM on weekends and holidays. The cost of supply and total consumption in the residential segment were obtained, respectively, from the Brazilian National System Operator (ONS) and Electric Energy Agency (ANEEL). The results obtained in fifteen previous experiments were used to estimate hypotheticals price elasticity and elasticity of substitution. Two types of rates were tested in scenarios: TOU and TOU with CPP. The results were lower than expected. An analysis is made to try to understand the reasons for this answer.

Os consumidores residenciais de energia elétrica no Brasil pagam um preço constante pela mesma em qualquer horário do dia, a despeito da variação constante nos custos de oferta. Isto não é economicamente eficiente. Para se atingir esta eficiência a implantação de uma tarifa inteligente se faz necessária, questão mais factível com o advento das redes inteligentes. Este trabalho busca antever se este desenvolvimento é custo efetivo ou não. Em primeiro lugar, os conceitos de redes inteligentes e de medidores avançados são apresentados. Em segundo lugar, são apresentados os conceitos de resposta da demanda e se demonstra porque o preço da eletricidade, para o consumidor final, deve ser maior na ponta do que fora da ponta. Por fim, se busca fazer uma análise custo benefício de um projeto hipotético de Infraestrutura de Leitura Avançada, desenvolvido por uma distribuidora de energia da região Centro Oeste do Brasil, a partir do estudo de cenários. Esse projeto hipotético ocorre num horizonte de dez anos, entre 2014 e 2023. O primeiro passo foi o desenvolvimento de campanhas de medição entre os anos de 2012 e 2013. Usando os dados aí obtidos, duas curvas de carga horárias foram desenvolvidas, uma para os dias úteis e a outra para finais de semana e feriados. O horário de pico é entre as 19 e as 22 horas nos dias úteis, e das 18 as 23 horas nos finais de semana e feriados. O custo da oferta e o consumo total de eletricidade foram obtidos, respectivamente, no Operador Nacional do Sistema e na Agência Nacional de Energia Elétrica. Os resultados obtidos em 15 experimentos prévios foram usados para estimar as hipotéticas elasticidades preço e elasticidades de substituição. Duas modalidades tarifárias foram testadas nos cenários: Tarifa Pelo Horário de Uso e Tarifa Pelo Horário de Uso com Preço de Pico Crítico. Os resultados obtidos ficaram aquém dos conceitualmente previstos. Uma análise é feita para tentar entender a razão desta resposta. / Residential customers in Brazil pay a constant price throughout the day, despite the large time variation in costs of supply. It is not economically efficient. It is necessary to set it to costumers with smart rates, and this possibility is getting closer from the development of smart grids. This work aims understand in advance if this deployment is cost-effective or not. Firstly, the concepts of Smart Grids, AMR (Automatic Meter Reading) and AMI (Advanced Metering Infrastructure) are presented. Secondly, concepts of demand response are described, and there is a demonstration of the reasons why electricity peak prices must be higher than off-peak prices. Thirdly, we seek to make a cost-benefit analysis for a hypothetical AMI project installation to residential customers, served by a utility in the Middle West of Brazil, under some potential scenarios. This hypothetical project runs in a ten year horizon (2014-2023). The first step was to perform measurement campaigns in 2012 and 2013. Using the data obtained, two residential hourly load curves were developed, one for weekdays and another for weekends and holidays. Peak time occurs between 7 and 10 PM in weekdays, and from 6 to 11 PM on weekends and holidays. The cost of supply and total consumption in the residential segment were obtained, respectively, from the Brazilian National System Operator (ONS) and Electric Energy Agency (ANEEL). The results obtained in fifteen previous experiments were used to estimate hypotheticals price elasticity and elasticity of substitution. Two types of rates were tested in scenarios: TOU and TOU with CPP. The results were lower than expected. An analysis is made to try to understand the reasons for this answer.

This dissertation contributes to theoretical and empirical studies in microeconomics, with a focus on evaluating policy relevant problems to provide new insights into questions. Within labor economics, I strive to understand the labor market returns to skills, taking into consideration the business cycle. In the first chapter, I investigate how the labor market returns to cognitive skills and social skills vary during recessions. In the second chapter, I examine the short-, medium and long-term career outcomes of college graduates as a function of economic conditions at graduation and both cognitive and social skills. In the third chapter, within information economics, I study how asymmetric information and demand uncertainty influence pricing strategies through learning. In Chapter 1, I examine how labor market returns to cognitive skills and social skills vary with the business cycle over the past 20 years, using data from the NLSY79 and the NLSY97. Exploiting a comparable set of cognitive and social skill measures across survey waves, I show that an increase in the unemployment rate led to higher demand for cognitive skills in the 2000s. High unemployment also sorted more workers into information use intensive occupations that require computer skills in the 2000s, but it sorted more workers into routine occupations in the 1980s and 1990s. This evidence suggests that recessions accelerate the restructuring of production toward routine-biased technologies. I also find that the returns to social skills increase during periods of high unemployment, though only in terms of the likelihood of full-time employment for experienced workers. Furthermore, an increase in unemployment increases the social skill task intensity of a worker's occupation in the 2000s, while it shows the contrary in the 1980s and 1990s. Based on these results, I argue that routine-biased technological change may not readily substitute for workers in tasks requiring interpersonal interaction, and therefore such technologies demand experienced laborers who have high social skills during recessions. In Chapter 2, I study the impacts of entry conditions on labor market outcomes to cognitive and social skills for the US college graduating classes of 1979–1989. Using the National Longitudinal Survey of Youth 1979, I find that Workers with higher cognitive skills are more likely to be employed, find job more quickly and have higher-quality employment, while those with higher social skills voluntarily switch jobs more often. I also show that graduating in a worse economy intensifies the roles of social skills, allowing workers with higher social skills to catch up more quickly from poor initial conditions by switching jobs more often. This could partly explain why wage returns to cognitive skills declines but wage returns to social skills increases from graduating in recessions. In Chapter 3, we consider a dynamic pricing problem facing a seller who has private information about the quality of her good, but is uncertain about the arrival rate of buyers. Restricting attention to the equilibria in which the high-quality seller insists on a constant price, we show that the low-quality seller's expected payoff and equilibrium pricing strategy crucially depend on buyers' knowledge about the demand state. If they are also uncertain about the demand state, then demand uncertainty increases the low-quality seller's expected payoff, and her optimal pricing strategy is to offer a high price initially and drop it to a low price later. If buyers know the demand state, then demand uncertainty does not affect the low-quality seller's expected payoff, and a simple cutoff pricing strategy cannot be a part of equilibrium. In the latter case, we show that there exists an equilibrium in which the low-quality seller begins with a low price, switches up to a high price, and eventually reverts back to the low price.

The airline industry is facing unprecedented challenges in generating sufficient revenues to stay in business. Airlines must capture the greatest revenue yield from every flight by leaving no seats unsold and not over filling the cabin with discount fares. To succeed in doing the above airlines must be able to accurately forecast each of their market segments, manage product andprice availability to maximize revenue and react quickly to competitive changes in the market place. Thus seat inventory control and ticket pricing form the two major tools of revenue management. The focus of this paper is to consolidate the ideas of seats inventory control and pricing in order to maximize the revenues generated by an airline network. A continuous time yield management model for a network with multiple legs, multiple fare classes and dynamic price changes for all fare classes is considered. Each fare class has a set of fares from which the optimal fare is chosen based upon the Minimum Acceptable Fare (MAF) which performs the critical role in the decision process. A machine Learning based algorithm, EMSRa based and EMSRb based algorithm for obtaining dynamic policies for combined pricing and allocation. The algorithms are implemented for a sample network with eight cities, eleven logs, thirty origin-destinations(ODs), three fare classes, three levels of fares in each class and ninety itineraries.

The rapid growth in demand due to the emergence of mobile communication services with variable rates, coupled with the resource scarcity of mobile air interface, has encouraged researchers to find technological solutions to increase spectral efficiency in order to support different levels of Quality of Service (QoS). Radio resource management (RRM) plays a major role in QoS provisioning and congestion control for wireless networks. The main problem with the congestion control mechanisms provided by current RRM schemes is that they are mostly reactive, triggered only when congestion occurs. The common, traditional solution to congestion has been for system planners to over-engineer a network by assigning more resources than are necessary. This approach is very costly because busy periods are usually brief, causing the network to be often under-utilised outside of these periods. Current static, usage-based pricing models also fail to assist in traffic shaping to even out loads. Economic modelling offers a new perspective into current RRM schemes and enables efficient utilisation of scarce resources and congestion prevention based on concepts such as utility, price, Pareto optimality and game theory. Dynamic pricing has been proposed as a mechanism to encourage users to adapt their resource consumption level according to network conditions. A good pricing model can provide the necessary positive incentives to increase users’ arrival rate when the network load is relatively low and negative incentives for users to defer their usage when the load is relatively high. In this dissertation, we propose an economic framework for pricing and RRM for 3G and beyond systems. Our aim is two-fold: to calculate an optimal integrated dynamic pricing and RRM policy; and to allocate scarce network resources in a fair and Pareto-optimal manner. The optimal integrated dynamic pricing and RRM policy is computed based on the stochastic distribution of users’ budget, arrivals, handoffs and departures. Our results show that the integrated policy is superior in terms of average reward improvement and congestion prevention to current schemes that use static pricing models. In interferencebased networks such as WCDMA, we suggest users be charged according to their noise rise factor, i.e. an estimate of the amount of interference generated by the call. This interference-based pricing model improves on the conventional load-based model in by delivering higher revenue and lower call blocking and handoff probabilities. Using the axiomatic bargaining concepts from cooperative game theory, we derive a class of fair and Pareto-optimal bargaining solutions that allocate wireless resources based on users’ minimum and maximum rate requirements. We propose two models: symmetric and asymmetric. In the latter, resource is allocated according to the price paid by the users. An important significance of the asymmetric bargaining model is that this solution is still Pareto-optimal and fair according to the users’ bargaining power. Our approach is also a departure from current works using noncooperative game theory that can only achieve an inefficient outcome, i.e. the Nash equilibrium; or cooperative game theory that focus on only one solution on the Pareto-optimal boundary. By analysing a range of bargaining solutions instead of specific ones, operators can proceed to select the best outcome out of these Pareto-optimal solutions based on criteria like revenue. / Thesis (Ph.D) -- University of Adelaide, School of Electrical and Electronic Engineering, 2007

Fluid dynamics models provide a powerful deterministic technique to approximate stochasticity in a variety of application areas. In this paper, we study two classes of fluid models, investigate their relationship as well as some of their applications. This analysis allows us to provide analytical models of travel times as they arise in dynamically evolving environments, such as transportation networks as well as supply chains. In particular, using the laws of hydrodynamic theory, we first propose and examine a general second order fluid model. We consider a first-order approximation of this model and show how it is helpful in analyzing the dynamic traffic equilibrium problem. Furthermore, we present an alternate class of fluid models that are traditionally used in the context of dynamic traffic assignment. By interpreting travel times as price/inventory-sojourn-time relationships, we are also able to connect this approach with a tractable fluid model in the context of dynamic pricing and inventory management. Finally, we investigate the relationship between these two classes of fluid models. / Singapore-MIT Alliance (SMA)

No description available.

Revenue management is a concept aimed to maximize capacity utilization and through that maximize revenues. It originated in the airline industry in the 70’s and due to its effectiveness  quickly spread to other sectors of the service industry. Today it is used in several industries like hotels, television and radio broadcasters, and energy transition companies to name a few. Since revenue management was developed in and for the service industry, most studies on revenue management are done on the service industry, creating a rather large research cap. Recently this concept has spread to the manufacturing industry as well. Despite this, there is very limited research done on revenue management in the manufacturing industry. Therefore, this paper’s aim is to partially filling this research gap by studying capacity management and pricing strategies (two mechanisms of revenue management), and how they have been shaped when implemented in a manufacturing company. This paper was done with a case study done on a multinational manufacturing company, who recently implemented revenue management. Interviews were conducted with people in key positions with good insight to the usage of revenue management in this company. Some of the most important result was that in this manufacturing company it is not possible to nest capacity on a customer segment level. However, in this company nesting was done on a market level instead. Also the pricing strategy differed between the service industry theory and this company. Instead of having a dynamic price that changed the total price up or down to change demand, this company had more of a fixed total price, and instead added more features to the product, decreasing the profit margin. The conclusion was drawn that the industry characteristics of the manufacturing industry have forced a rather large modification of revenue management. However, since this was a qualitative case study, no generalizing conclusions for the entire manufacturing industry can be drawn.