In the last decade, the landscape of storage hardware and software has changed considerably. Storage hardware has diversified from hard disk drives and solid state drives to include persistent memory (PMEM) devices such as phase change memory (PCM) and Flash-backed DRAM. On the software side, the increasing adoption of cloud services for building and deploying consumer and enterprise applications is driving the use of cloud storage services. Cloud providers have responded by providing a plethora of choices of storage services, each of which have unique performance characteristics and pricing. We argue this variety represents an opportunity for modern storage systems, and it can be leveraged to improve operational costs of the systems. We propose that storage tiering is an effective technique for balancing operational or de- ployment costs and performance in such modern storage systems. We demonstrate this via three key techniques. First, THMCache, which leverages tiering to conserve the lifetime of PMEM devices, hence saving hardware upgrade costs. Second, CAST, which leverages tiering between multiple types of cloud storage to deliver higher utility (i.e. performance per unit of cost) for cloud tenants. Third, we propose a dynamic pricing scheme for cloud storage services, which leverages tiering to increase the cloud provider's profit or offset their management costs. / Master of Science / Storage and retrival of data is one of the key functions of any computer system. Improvements in hardware and software related to data storage can help computer users store (a) store the data faster, which makes for overall faster performance; and (b) increase the storage capacity, which helps store the increasing amount of data generated by modern computer users. Typically, most computers are equipped with either a hard disk drive (HDD) or, the newer and faster, solid state drive (SSD) for data storage. In the last decade however, the landscape of data storage hardware and software has advanced considerably. On the hardware side, several hardware makers are introducing persistent memory (PMEM) devices, which provide very high speed, high capacity storage at reasonable price points. On the software side, the increasing adoption of cloud services by software developers that are building and operating consumer and enterprise applications is driving the use of cloud storage services. These services allow the developers to store a large amount of data without having to manage any physical hardware, paying for the service on a usage-based pricing structure. However, every application’s speed and capacity needs are not the same; hence, cloud service providers have responded by providing a plethora of choices of storage services, each of which have unique performance characteristics and pricing. We argue this variety represents an opportunity for modern storage systems, and it can be leveraged to improve the operating costs of the systems. Storage tiering is a classical technique that involves partitioning the stored data and placing each partition in a different storage device. This lets the applications use mulitple devices at once, taking advantage of each’s sterngths and mitigating their weaknesses. We propose that storage tiering is a relevant and effective technique for balancing operational or deployment costs and performance in modern storage systems such as PMEM devices and cloud storage services. We demonstrate this via three key techniques. First, THMCACHE, which leverages tiering between multiple types of storage hardware to conserve the lifetime of PMEM devices, hence saving hardware upgrade costs. Second, CAST, which leverages tiering between multiple types of cloud storage services to deliver higher utility (i.e. performance per unit of cost) for software developers using these services. Third, we propose a dynamic pricing scheme for cloud storage services, which leverages tiering between multiple cloud storage services to increase the cloud service provider’s profit or offset their management costs.

Since the last United Nations Climate Change Conference in 2015 in Paris (the COP 21), world leaders acknowledged climate change. There is no need any more to justify the switch from fossil fuel-based to renewable energy sources. Nevertheless, this transition is far from being straightforward. Besides technologies that are not yet mature -- or at least not always financially viable in today's economy -- the power grid is currently not ready for a rapid and massive integration of renewable energy sources. A main challenge for the power grid is the inadequacy between electric production and consumption that will rise along with the integration of such sources. Indeed, due to their dependence on weather, renewable energy sources are intermittent and difficult to forecast with today's tools. As a commodity, electricity is a quite distinct good for which there must be perfect adequacy of production and consumption at all time and characterized by a very inelastic demand. High shares of renewable energy sources lead to high price volatility and a higher risk to jeopardize the security of supply. Additionally, the switch to renewable energy sources will lead to an electrification of loads and transportation, and thus the emergence of new higher-consumption loads such as electric vehicles and heat pumps. These new and higher-consumption loads, combined with the population growth, will cause over-rated power load increases with less predictable load patterns in the future.This work focuses on issues specific to the distribution power grid in the context of the current energy transition. Traditional low-voltage grids are perhaps the most passive circuits in power grids. Indeed, they are designed primarily using a fit and forget approach where power flows go from the distribution transformer to the consumers and no element has to be operated or regularly managed. In fact, low-voltage networks completely lack observability due to very low monitoring. The distribution grid will especially undergo drastic changes from this energy transition. Distributed sources and new high-consumption -- and uncoordinated -- loads result in new power flow patterns, as well as exacerbated evening peaks for which it is not designed. The consequences are power overloads and voltage imbalances that deteriorate grid components, such as a main asset like the medium-to-low voltage transformer. Additionally, the distribution grid is characterized by end-users that pay a price for electricity that does not reflect the grid situation -- that is, mostly constant over a year -- and allow little to no actions on their consumption.These issues have motivated authorities to propose a global approach to ensure security of electricity supply at short and medium-term. The latter requires, among others, the development of demand response programs that encourage users to take advantage of load flexibility. First, we propose adequate electricity pricing structures that will allow users to unlock the potential of such demand response programs; namely, dynamic pricings combined with a prosumer structure. Second, we propose a fast and robust two-level optimization, formulated as a mixed-integer linear program, that coordinates flexible loads. We focus on two types of loads; electric vehicles and heat pumps, in an environment with solar PV panels. The lower level aims at minimizing individual electricity bills while, at the second level, we optimize the power load curve, either to maximize self-consumption, or to smoothen the total power load of the transformer. We propose a parametric study on the trade-off between only minimizing the individual bills versus only optimizing power load curves, which have proven to be antagonist objectives. Additionally, we assess the impact of the rising share of flexible loads and renewable energy sources for scenarios from today until 2050. A macro-analysis of the results allows us to assess the benefits of load flexibility for every actor of the distribution grid, and depending on the choice of a pricing structure. Our optimization has proved to prevent evening peaks, which increases the lifetime of the distribution transformer by up to 200%, while individual earnings up to 25% can be made using adequate pricings. Consequently, the optimization significantly increases the power demand elasticity and increases the overall welfare by 10%, allowing the high shares of renewable energy sources that are foreseen. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

<div>The app-based taxi services (ATS) has disrupted the traditional (street-hailing) taxi services (TTS) leading to transformative changes in the urban taxi markets and its impacts on mobility, design and environment. However, the current modeling of these new mobility markets is limited in its understanding of: (1) the underlying factors that influence the growth of the ATS market; (2) the competition of ATS and TTS markets; (3) pricing in the ATS market; (4) system wide tools to understand the impacts of the market. The overarching goal of this dissertation is to address four fundamental processes of taxi system, ranging from demand generation, supply generation and exiting, dynamic pricing generation, and vehicle-passenger matching over road network. This dissertation achieves these goals by using original large scale datasets to characterize disruptive changes in mobility, understand strategic behaviors of stakeholders, and formulate system dynamics.</div><div> </div><div>This dissertation develops various modeling structures and estimation methods, motivated from statistical, econometric, machine learning, and stochastic approaches. First, we adapt multiple econometric models for demand, supply, and platform-exiting (offline) behaviors, including mixture model of spatial lag and Poisson regression and mixture model of spatial lag and panel regression. It is apparent that all proposed econometric models should be corrected with spatial lag due to significant spatial autocorrelations. The results indicate effectiveness of dynamic pricing in controlling demand, however, it also shows no impacts on driver's online and offline behaviors. Then a dynamic pricing generation problem is formulated with multi-class classification. This model is empirically validated for the impacts of demand and supply in dynamic price generation and the significant spatial and temporal heterogeneity. Last, we propose a queueing network consisting of taxi service queues for vehicle-passenger matching and road service queue for vehicle movements at homogeneous spatial units. The method captures stochasticity in vehicle-passenger matching process, and more importantly, formulates the interactions with urban road traffic.</div><div> </div><div>In summary, this dissertation provides a holistic understanding of fundamental processes that govern the rapid rise in ATS markets and in developing quantitative tools for the system wide impacts of this evolving taxi markets. Taken together, these tools are transformative and useful for city agencies to make various decisions in the smart mobility landscape. </div>

Revenue Management (RM) has become one of the most successful application areas of Operation Research. What started off as an obscure practice among few airlines in U.S in early seventies, has attained the status of mainstream business practice, thanks to the major success enjoyed by companies applying RM. Over the same period, academic and industrial research on the methodology of RM has also grown rapidly. Despite the vast technical literature on the subject of revenue management, relatively few papers explicitly model the customer’s choice behavior. Such a behavior of customers could have major impact on revenue realized by an organization. Motivated by this, we focus on addressing the problem faced by a seller who serves customers exhibiting buy-down behavior. We address two important problems faced by a seller with few perishable goods. His objective is to obtain maximum revenue possible by sales of his perishable goods. The seller now potentially faces the problem of fixing the price of the products and then control the availability of products so as to maximize his revenue by minimizing the number of customers who buy-down. The first problem is the multi-product pricing problem where we consider a monop- olistic market situation in which a seller has some quantities of perishable goods under his disposal. The seller has the option of adding few additional features to the base product(perishable good) and thereby differentiating the products to cater to different market segments. Adding each additional feature involves certain cost and there are no restrictions on the availability of the features except that a feature can be added to the base product atmost once . The customers are price-sensitive and the seller is aware of the price-demand relationship of the various customer segments. A customer looking for a product buys the product if and only if the price is less than his reservation price. The sellers’ problem is to identify the price and bundling of features for the various customer segments so as to generate maximum possible revenue. We develop a Mixed integer non-linear mathematical programming model for the problem. We then split the problem into pricing problem and bundling problem and solve them sequentially. We finally provide a numeric example to illustrate the solution procedure. Once the prices are fixed, the next problem is to control the availability of products so as to prevent the buy-down behavior of the customers. We deal with the situation of a seller with two substitutable products. The price of both products are fixed over entire selling period. In a traditional control mechanism structure if the sequence of arrival of customers are known, then it becomes trivial to solve the problem of setting control limits which would prevent buy-down behavior. But in reality it never happens that the seller knows the arrival sequence. Hence in this study to isolate the effect of arrival sequence from other complexities like demand variability, we assume a deterministic demand for both the products but the arrival sequence is randomized. We initially analyze the above described problem and develop a static control mech- anism. We show that the static control mechanism is asymptotically equivalent to the traditional selling mechanism. Then we move on to make modification in the static con- trol mechanism and make it a dynamic control mechanism such that it will respond to the buy-down customers. In order to analyze the performance of dynamic control mechanism, we build a simulation model that would compare traditional selling mechanism and dynamic control mechanism. Statistical analysis is then done on the simulation results. It is shown that for all values of buy-down proportion, on an average the dynamic control mechanism outperforms the traditional control mechanism. Further there is a trend in revenues generated depending upon the buy-down proportion which is also explained. The chapter concludes with operating guidelines for better revenue realization. The organization of the thesis is as follows. In chapter 2, we present the literature survey. We start off with the history of RM and proceed to discuss the inventory control problems in RM in detail. Then we discuss literatures dealing with customer choice behavior. In chapter 3, we define and model the multi - product pricing problem. We present a mixed integer non-linear mathematical program to model the pricing problem. The solution to this problem is divided into two sub problems - the pricing problem and the bundling problem. Solution methodologies for both sub - problem are given and the chapter concludes with a numerical illustration for a 3 - product pricing problem. In chapter 4, we define and address the inventory control problem for a two product case when customers exhibit buy-down nature. We develop a static control mechanism and study its properties. Then we move on to the dynamic control mechanism which would suit real - world conditions. Finally we study the quality of developed methodology using statistical testing methods.

ABSTRACT Date:  2021-06-03 Level:  Bachelor Thesis in Business Administration, 15 cr Institution: School of Business, Society and Engineering, Mälardalen University Authors: Aryan Singh   Omar Eyad  Shaheen Mohammad  Title:  How Market Intelligence Helps With Pricing Tutor:  Ali Farashah Keywords: Digitalization, Business Intelligence (BI), Market Intelligence (MI), Competitive Intelligence (CI), Pricing, Decision Making, Dynamic Pricing Model, Price Authority Research Question: How does MI help in pricing decisions of the European market in Systemair Purpose: The purpose of this research is to investigate if Market Intelligence (MI) has any effect on pricing decisions within the European market in Systemair Group. Since the field of MI in pricing decisions is explorative, this study will conduct thorough interviews with the goal of getting a deep understanding of how MI can help with pricing decisions. This study also aims to contribute to the research in this subject. Method: This study has been conducted in a qualitative manner on the case company Systemair Group. Primary data was collected through academic articles found via the library of Mälardalen University and scientific databases. The research was based on 5 semi-structured interviews conducted online with employees of Systemair. Conclusion: MI plays an important role in pricing. It gathers real time market data that is objective to feelings from the sales team or other employees. Factory capacity will be optimized with the evolution of MI, profit margin will be set higher than before and so this will result in a push in the overall price level of Systemair products. Value-Based Selling points and Resources are an integral part of the dynamic pricing model, specifically in Strategic Input and Data Input respectively.

The pervasive availability of streaming data from various sources is driving todays’ enterprises to acquire low-latency big data streaming applications (BDSAs) for extracting useful information. In parallel, recent advances in technology have made it easier to collect, process and store these data streams in the cloud. For most enterprises, gaining insights from big data is immensely important for maintaining competitive advantage. However, majority of enterprises have difficulty managing the multitude of BDSAs and the complex issues cloud technologies present, giving rise to the incorporation of cloud service brokers (CSBs). Generally, the main objective of the CSB is to maintain the heterogeneous quality of service (QoS) of BDSAs while minimizing costs. To achieve this goal, the cloud, although with many desirable features, exhibits major challenges — resource prediction and resource allocation — for CSBs. First, most stream processing systems allocate a fixed amount of resources at runtime, which can lead to under- or over-provisioning as BDSA demands vary over time. Thus, obtaining optimal trade-off between QoS violation and cost requires accurate demand prediction methodology to prevent waste, degradation or shutdown of processing. Second, coordinating resource allocation and pricing decisions for self-interested BDSAs to achieve fairness and efficiency can be complex. This complexity is exacerbated with the recent introduction of containers. This dissertation addresses the cloud resource elasticity management issues for CSBs as follows: First, we provide two contributions to the resource prediction challenge; we propose a novel layered multi-dimensional hidden Markov model (LMD-HMM) framework for managing time-bounded BDSAs and a layered multi-dimensional hidden semi-Markov model (LMD-HSMM) to address unbounded BDSAs. Second, we present a container resource allocation mechanism (CRAM) for optimal workload distribution to meet the real-time demands of competing containerized BDSAs. We formulate the problem as an n-player non-cooperative game among a set of heterogeneous containerized BDSAs. Finally, we incorporate a dynamic incentive-compatible pricing scheme that coordinates the decisions of self-interested BDSAs to maximize the CSB’s surplus. Experimental results demonstrate the effectiveness of our approaches.

Cada vez mais, a competividade fortalece a filosofia de administração empresarial baseada na orientação para o cliente e para o lucro. A busca de novas formas de relacionamento com os clientes requer instrumentos que possam personalizar inclusive os preços que compõem a equação de valor de cada um. O que temos visto, na prática, são ações que não consideram a dinâmica das forças do mercado, representada pelas curvas de oferta e demanda, e por isso expõem a organização ao risco de propor um processo de troca que não atenda ao preceito de que deve beneficiar tanto o vendedor quanto o comprador, como fator mais importante para a manutenção de um relacinamento de longo prazo. Os instrumentos apresentados como equações matemamáticas, pretendem viabilizar a adoção de forma monitorada com os objetos da gestão econômica. Como benefício complementar, os intrumentos por requererem regras claras, objetivas e bem delimitadas para os processos de negociação, permitem compartilhar de maneira justa, a responsabilidade sobre o atingimento dos resultados através de transações comerciais. / Competitiveness fortifies the entrepreneurial management philosophy that prioritizes more and more, customers and profit. The search for new relationship between companies and customers requires appropriate tools to even, embody the prices that composes the equation of value. We have noticed according to the procedures, actions disrespecting the dynamic of market forces, represented by supply and demand curves and, as a consequence, the companies can be involved in a trading process that can not serve neither the seller nor the buyer, damaging the relationship in the long run. The algorithms presented as mathematical equation aim at enabling the approval of the dynamic prices in trading processes to support their own flexibility in a controlled way that is connected to the goals of the economic management. As a complementary benefit, the algorithms require clear, delimitative and objective rules for the dealing processes and make possible sharing in a fair way, the responsability for the achievement of the results through the trading negotiations.

在未來的無線點對點環境中，由於資訊將被賦予金錢上的價值，故核心議題將不在於如何解決資訊便車者問題，而是必須針對有價資訊制定一有效的定價模式以促進個體提供差異化服務的意願。本研究所提出之情境式小額定價模式，以價值導向定價為基礎，除了針對個體本身資源限制考量而設計之外，更考量服務本身之特性(如無形性、不可分割性、異質性與易逝性)，因此可有效地針對個體目前所處的情境需求以協助服務買方與賣方分別制定服務價格策略與價格談判策略，讓買賣雙方可快速地達成協議。 本研究期望藉由情境式定價與談判機制的提出，能對WP2P 的無線應用服 務發展有所貢獻，並期望讓使用者在動態且即時的環境下，能有效地促進資訊分享的意願與流通，進而能恣意地享受行動服務所帶來的全新生活體驗。 / In the foreseeable Wireless Peer-to-Peer (WP2P) environments (in which information traded is associated with monetary value), one of the key issues in WP2P will focus on how to build efficient pricing strategies to facilitate the peers’ willingness of offering differentiated services (rather than the status–quo of merely resolving the free rider problems). Accordingly, this paper presents a contextualized micro pricing strategy for e-services operating in distributed WP2P environments. The pricing strategy grounding in the concept of value-based pricing not only takes mobile device restrictions and attributes of the surrounding context (ex. time, location) into account, but also regards the unique features of services (intangibility, inseparability, heterogeneity, perishability) to assist service buyers and sellers to rapidly come to a deal with each other in terms of a lightweight pricing/bargaining process. The contribution of the proposed contextualized micro pricing strategy is to improve peers’ willingness of furnishing differentiated services and to enhance the distribution of the service resources amid the WP2P environments.

Cada vez mais, a competividade fortalece a filosofia de administração empresarial baseada na orientação para o cliente e para o lucro. A busca de novas formas de relacionamento com os clientes requer instrumentos que possam personalizar inclusive os preços que compõem a equação de valor de cada um. O que temos visto, na prática, são ações que não consideram a dinâmica das forças do mercado, representada pelas curvas de oferta e demanda, e por isso expõem a organização ao risco de propor um processo de troca que não atenda ao preceito de que deve beneficiar tanto o vendedor quanto o comprador, como fator mais importante para a manutenção de um relacinamento de longo prazo. Os instrumentos apresentados como equações matemamáticas, pretendem viabilizar a adoção de forma monitorada com os objetos da gestão econômica. Como benefício complementar, os intrumentos por requererem regras claras, objetivas e bem delimitadas para os processos de negociação, permitem compartilhar de maneira justa, a responsabilidade sobre o atingimento dos resultados através de transações comerciais. / Competitiveness fortifies the entrepreneurial management philosophy that prioritizes more and more, customers and profit. The search for new relationship between companies and customers requires appropriate tools to even, embody the prices that composes the equation of value. We have noticed according to the procedures, actions disrespecting the dynamic of market forces, represented by supply and demand curves and, as a consequence, the companies can be involved in a trading process that can not serve neither the seller nor the buyer, damaging the relationship in the long run. The algorithms presented as mathematical equation aim at enabling the approval of the dynamic prices in trading processes to support their own flexibility in a controlled way that is connected to the goals of the economic management. As a complementary benefit, the algorithms require clear, delimitative and objective rules for the dealing processes and make possible sharing in a fair way, the responsability for the achievement of the results through the trading negotiations.

In this thesis, we consider stochastic systems which arise in different real-world application contexts. The first problem we consider is based on product adoption and pricing. A monopolist selling a product has to appropriately price the product over time in order to maximize the aggregated profit. The demand for a product is uncertain and is influenced by a number of factors, some of which are price, advertising, and product technology. We study the influence of price on the demand of a product and also how demand affects future prices. Our approach involves mathematically modelling the variation in demand as a function of price and current sales. We present a simulation-based algorithm for computing the optimal price path of a product for a given period of time. The algorithm we propose uses a smoothed-functional based performance gradient descent method to find a price sequence which maximizes the total profit over a planning horizon. The second system we consider is in the domain of sensor networks. A sensor network is a collection of autonomous nodes, each of which senses the environment. Sensor nodes use energy for sensing and communication related tasks. We consider the problem of finding optimal energy sharing policies that maximize the network performance of a system comprising of multiple sensor nodes and a single energy harvesting(EH) source. Nodes periodically sense a random field and generate data, which is stored in their respective data queues. The EH source harnesses energy from ambient energy sources and the generated energy is stored in a buffer. The nodes require energy for transmission of data and and they receive the energy for this purpose from the EH source. There is a need for efficiently sharing the stored energy in the EH source among the nodes in the system, in order to minimize average delay of data transmission over the long run. We formulate this problem in the framework of average cost infinite-horizon Markov Decision Processes[3],[7]and provide algorithms for the same.