In this paper, we introduce a fluid model of dynamic pricing and inventory management for make-to-stock manufacturing systems. Instead of considering a traditional model that is based on how price affects demand, we consider a model that relies on how price and level of inventory affect the time a unit of product remains in inventory. Our motivation is based on the observation that in inventory systems, a unit of product incurs a delay before being sold. This delay depends on the unit price of the product, prices of competitors, and the level of inventory of this product. Moreover, delay data is not hard to acquire and is internally controlled and monitored by the manufacturer. It is interesting to notice that this delay is similar to travel times incurred in a transportation network. The model of this paper includes joint pricing, production and inventory decisions in a competitive, capacitated multi-product dynamic environment. In particular, in this paper we (i) introduce a model for dynamic pricing and inventory control that uses delay rather then demand data and establish connections with traditional demand models, (ii) study analytical properties of this model, (iii) establish conditions under which the model has a solution and finally, (iv) establish an algorithm that solves efficiently a discretized version of the model.

Tillämpningen av dynamisk prissättning har förenklats genom 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 således prissättas, utefter kundens köpvillighet. 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 pristransparens 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 sambandet mellan kundattityd och dynamisk prissättning. För att undersöka kundattityd skapas en modell utifrån teori med komponenterna kundvärde, kundnöjdhet, kvalitet, kundlojalitet, förtroende och rättvisa. Modellen används för att undersöka hur en prisändring påverkar kundens attityd mot dynamisk prissättning. Studiens analys grundar sig på empiriska 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 hos kunderna.

In this thesis we investigate important issues in the area of dynamic pricing for revenue management. Studying the effect of competition and strategic consumer behavior, we characterize the dynamic pricing policies for retailers who sell homogeneous goods in multi-period, discrete time, finite horizon settings. In the first essay an impatient consumer visits only one of two competing retailers in each period. If he does not purchase the good, he visits the competing retailer in the ensuing period. Compared to the corresponding single store monopoly, when the consumer’s valuation is uniformly distributed, prices decline exponentially rather than linearly, with a dramatically lower initial price, and a substantially lower system profit. The model is extended to accommodate many consumers, who may be either identical or similar, a more general valuation distribution, and situations wherein capacities are limited. The base case of a centralized two-store monopoly is also examined. In the second essay the consumer may return to the same retailer with some certain probability. This probability is either affected by market structure characteristics, or it may depend on the consumer’s experience at the last store visited. The robustness of the exponential decline of prices is reinforced. It occurs even when a strong retailer faces competition from a relatively much weaker retailer. We investigate the impact of the return probabilities on prices, profits, and consumer surplus. The model is extended to an oligopoly, and to situations with many similar consumers. The effect of strategic consumer behavior on prices and profits is revealed in the third essay. Characterizing the pricing policies arising in a two-period monopoly and duopoly settings, we find that strategic consumer behavior inflicts larger losses to a duopoly than to a monopoly. A lower strategic consumers’ discounting factor, which is beneficial to a monopoly, may be harmful to a duopoly. Ignoring strategic consumer behaviour is costly to a monopoly, but may, on the other hand, be beneficial to a duopoly. An extension to three periods is studied, and with longer horizons the model is analyzed for the case when all the consumers are strategic.

In this thesis we investigate important issues in the area of dynamic pricing for revenue management. Studying the effect of competition and strategic consumer behavior, we characterize the dynamic pricing policies for retailers who sell homogeneous goods in multi-period, discrete time, finite horizon settings. In the first essay an impatient consumer visits only one of two competing retailers in each period. If he does not purchase the good, he visits the competing retailer in the ensuing period. Compared to the corresponding single store monopoly, when the consumer’s valuation is uniformly distributed, prices decline exponentially rather than linearly, with a dramatically lower initial price, and a substantially lower system profit. The model is extended to accommodate many consumers, who may be either identical or similar, a more general valuation distribution, and situations wherein capacities are limited. The base case of a centralized two-store monopoly is also examined. In the second essay the consumer may return to the same retailer with some certain probability. This probability is either affected by market structure characteristics, or it may depend on the consumer’s experience at the last store visited. The robustness of the exponential decline of prices is reinforced. It occurs even when a strong retailer faces competition from a relatively much weaker retailer. We investigate the impact of the return probabilities on prices, profits, and consumer surplus. The model is extended to an oligopoly, and to situations with many similar consumers. The effect of strategic consumer behavior on prices and profits is revealed in the third essay. Characterizing the pricing policies arising in a two-period monopoly and duopoly settings, we find that strategic consumer behavior inflicts larger losses to a duopoly than to a monopoly. A lower strategic consumers’ discounting factor, which is beneficial to a monopoly, may be harmful to a duopoly. Ignoring strategic consumer behaviour is costly to a monopoly, but may, on the other hand, be beneficial to a duopoly. An extension to three periods is studied, and with longer horizons the model is analyzed for the case when all the consumers are strategic.

In this thesis we investigate important issues in the area of dynamic pricing for revenue management. Studying the effect of competition and strategic consumer behavior, we characterize the dynamic pricing policies for retailers who sell homogeneous goods in multi-period, discrete time, finite horizon settings. In the first essay an impatient consumer visits only one of two competing retailers in each period. If he does not purchase the good, he visits the competing retailer in the ensuing period. Compared to the corresponding single store monopoly, when the consumer’s valuation is uniformly distributed, prices decline exponentially rather than linearly, with a dramatically lower initial price, and a substantially lower system profit. The model is extended to accommodate many consumers, who may be either identical or similar, a more general valuation distribution, and situations wherein capacities are limited. The base case of a centralized two-store monopoly is also examined. In the second essay the consumer may return to the same retailer with some certain probability. This probability is either affected by market structure characteristics, or it may depend on the consumer’s experience at the last store visited. The robustness of the exponential decline of prices is reinforced. It occurs even when a strong retailer faces competition from a relatively much weaker retailer. We investigate the impact of the return probabilities on prices, profits, and consumer surplus. The model is extended to an oligopoly, and to situations with many similar consumers. The effect of strategic consumer behavior on prices and profits is revealed in the third essay. Characterizing the pricing policies arising in a two-period monopoly and duopoly settings, we find that strategic consumer behavior inflicts larger losses to a duopoly than to a monopoly. A lower strategic consumers’ discounting factor, which is beneficial to a monopoly, may be harmful to a duopoly. Ignoring strategic consumer behaviour is costly to a monopoly, but may, on the other hand, be beneficial to a duopoly. An extension to three periods is studied, and with longer horizons the model is analyzed for the case when all the consumers are strategic. / Business, Sauder School of / Graduate

With the grasp of a smart grid in sight, discussions have shifted the focus of system security measures away from generation capacity; apart from modifying the supply side, demand may also be exploited to keep the system in balance. Specifically, Demand Response (DR) is the concept of consumer load modification as a result of price signaling, generation adequacy, or state of grid reliability. Implementation of DR mechanisms is one of the solutions being investigated to improve the efficiency of electricity markets and to maintain system-wide stability.  In a liberalized electricity sector, with a smart grid vision that is committed to market-based operation, end-users have now become the focal point of decision-making at every stage of the process in producing, delivering and consuming electricity. DR program implementation falls within the smart grid domain: a complex socio-technical energy system with a multiplicity of physical, economic, political and social interactions. This thesis thus employs both qualitative and quantitative research methods in order to address the ways in which residential end-users can become active DR flexibility providers in deregulated European electricity markets. The research focuses on economic incentives including dynamic pricing contracts, dynamic distribution price signals and the aggregation of load flexibility for participation in the various short-term electricity markets. / <p>The Doctoral Degrees issued upon completion of the programme are issued by Comillas Pontifical University, Delft University of Technology and KTH Royal Institute of Technology. The invested degrees are official in Spain, the Netherlands and Sweden, respectively.</p><p>QC 20160225</p> / Erasmus Mundus Joint Doctorate in Sustainable Energy Technologies and Strategies (SETS)

Smart communications devices are giving users instant access to applications that consume large amounts of data. These applications have different requirements on the network for delivery of data. In order to support these different applications, operators are required to support multiple service classes. Given the regulatory and technology constraints and the relatively high cost associated with wireless spectrum licensing and utilization, demand will exceed supply leading to congestion and overload conditions. In addition to new broadband radio technologies offering higher data rates, operators are looking at deploying alternate heterogeneous technologies, such as WLAN, to provide additional bandwidth for serving customers. It is expected that this will still fall short of providing enough network resources to meet the ITU requirement for 1% new call blocking probability. An economic mechanism that offers incentives to individuals for rational behavior is required in order in order to reduce the demand for network resources and resolve the congestion problem. The research in this dissertation demonstrates that the integration of a dynamic pricing with connection admission control mechanism for an operator deploying cooperative heterogeneous networks (e.g., LTE and WLAN) offering multiple QoS service classes reduces the new call blocking probability to the required 1% level. The experimental design consisted, first, of an analytical model of the CAC algorithm with dynamic pricing in a heterogeneous environment. The analytical model was subsequently validated through discrete-event simulation using Matlab.

Pricing decision is the minority of all important decisions which can apparently influence a firm's profit-making within extremely short time. In an era of meagre profit, firms cannot stand any more injury caused of mistake at pricing. However, lots of managers still make pricing decision according to their experience or the action of other competitors without any mechanism of price-determining based on their firms' resource condition. The subject of this research is to probe the abiding price-reducing strategy for fashion appearing firms. Fashion apparel is a kind of commodities with seasonality and popularity, and is an example of all perishable goods. For all sorts of characteristic such as the need for long lead time before production, short time span for sale , and the low salvage value after season...etc., it makes firms reduce price to close out inventories by the end of seasons to evade value loss. When it comes to price-reducing, the fashion apparel is quite different from other commodities. It is a kind of commodity which has speciality of phased and monotonicity on price reduction. Therefore, it lacks two kinds of elasticity which are price-adjusting at any time and adjusting the price range at will. For the characteristic of close interdependence between product and time, and the normal demand on price-reducing, fashion apparel firms need some decision tools which are more fast, correct, and practical than any other ones. With two main parameters which are 'the levels of unsold inventory' and ' the length of season remaining ' along with two parameters which are 'discount factor' and ' the salvage value after season ', this research constructs out an stochastic dynamic programming model to maximize the expect profit and offer an program for calculating the optimal price-reduced range and time. After the analysis of generality and sensitivity with this model, it is found that the characteristics of this model are in conformity with theoretical research and real phenomenon of market. Besides, it is suitable for various kinds of price elastic demand. Hence, this model can be proved to be able to extend to other similar industries with the same nature.

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.

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