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.

The thesis is focused on the optimization of models of transportation and transshipment problem with random demand, additional edges, and dynamic pricing. The theoretical part of the thesis introduces mathematical models of transportation. The software GAMS, which is used for the solution, is all so described. The practical part is a split among chapters and implements the described models by using real data.

It is commonly agreed among energy experts that smart meters (SMs) are the key component that will facilitate the transition towards the smart grid. Fast-peace innovations in the smart metering infrastructure (AMI) are exposing countless benefits that network operators can obtain when they integrate SMs applications into their daily operations.  Following the amendment in 2017, where the Swedish government dictated that all SMs should now include new features such as remote control, higher time resolution for the energy readings and a friendly interface for customers to access their own data; network operators in Sweden are currently replacing their SMs for a new model, also called the second generation of SMs. While the replacement of meters is in progress, many utilities like Hemab are trying to reveal which technical and financial benefits the new generation of SMs will bring to their operations.    As a first step, this thesis presents the results of a series of interviews carried out with different network operators in Sweden. It is studied which functionalities have the potential to succeed in the near future, as well as those functionalities that are already being tested or fully implemeneted by some utilities in Sweden. Furthermore, this thesis analyses those obstacles and barriers that utilities encounter when trying to implement new applications using the new SMs. In a second stage, an alarm system for power interruptions and voltage-quality events (e.g., overvoltage and undervoltage) using VisionAir software and OMNIPOWER 3-phase meters is evaluated. The results from the evaluation are divided into three sections: a description of the settings and functionalities of the alarm, the outcomes from the test, and a final discussion of potential applications. This study has revealed that alarm functions, data analytics (including several methods such as load forecasting, customer segmentation and non-technical losses analysis), power quality monitoring, dynamic pricing, and load shedding have the biggest potential to succeed in Sweden in the coming years. Furthermore, it can be stated that the lack of time, prioritization of other projects in the grid and the integration of those new applications into the current system seem to be the main barrier for Swedish utilities nowadays. Regarding the alarm system, it was found that the real benefits for network operators arrive when the information coming from an alarm system is combined with a topology interface of the network and a customer notifications server. Both applications could improve customer satisfaction by significantly reducing outage time and providing customers with real-time and precise information about the problems in the grid.

As urban population across the globe increases, the demand for adequate transportation grows. Several strategies have been suggested as a solution to the congestion which results from this high demand outpacing the existing supply of transportation facilities. High –Occupancy Toll (HOT) lanes have become increasingly more popular as a feature on today’s highway system. The I-95 Express HOT lane in Miami Florida, which is currently being expanded from a single Phase (Phase I) into two Phases, is one such HOT facility. With the growing abundance of such facilities comes the need for indepth study of demand patterns and development of an appropriate pricing scheme which reduces congestion. This research develops a method for dynamic pricing on the I-95 HOT facility such as to minimize total travel time and reduce congestion. We apply non-linear programming (NLP) techniques and the finite difference stochastic approximation (FDSA), genetic algorithm (GA) and simulated annealing (SA) stochastic algorithms to formulate and solve the problem within a cell transmission framework. The solution produced is the optimal flow and optimal toll required to minimize total travel time and thus is the system-optimal solution. We perform a comparative evaluation of FDSA, GA and SA non-linear programming algorithms used to solve the NLP and the ANOVA results show that there are differences in the performance of the NLP algorithms in solving this problem and reducing travel time. We then conclude by demonstrating that econometric iv forecasting methods utilizing vector autoregressive (VAR) techniques can be applied to successfully forecast demand for Phase 2 of the 95 Express which is planned for 2014

Several studies have been conducted within machine learning, and various variations have been applied to a wide spectrum of other fields. However, a thorough feasibility study within the payment processing industry using machine learning classifier algorithms is yet to be explored. Here, we construct a rule-based response vector and use that in combination with a magnitude of varying feature vectors across different machine learning classifier algorithms to try and determine whether individual transactions can be considered profitable from a business point of view. These algorithms include Naive-Bayes, AdaBoosting, Stochastic Gradient Descent, K-Nearest Neighbors, Decision Trees and Random Forests, all helped us build a model with a high performance that acts as a robust confirmation of both the benefits and a theoretical guide on the implementation of machine learning algorithms in the payment processing industry. The results as such are a firm confirmation on the benefits of data intensive models, even in complex industries similar to Swedbank Pay’s. These Implications help further boost innovation and revenue as they offer a better understanding of the current pricing mechanisms. / Många studier har utförts inom ämnet maskininlärning, och olika variationer har applicerats på ett brett spektrum av andra ämnen. Däremot, så har en ordentlig genomförbarhetsstudie inom betalningsleveransindustrin med hjälp av klassificeringsalgortimer har ännu ej utforskats. Här har vi konstruerat en regelbaserad responsvektor och använt den, tillsammans med en rad olika och varierande egenskapvektorer på olika maskininlärningsklassificeringsalgoritmer för att försöka avgöra ifall individuella transaktioner är lönsamma utifrån företagets perspektiv. Dessa algoritmer är Naive-Bayes, AdaBoosting, Stokastisk gradient medåkning, K- Närmaste grannar, beslutsträd och slumpmässiga beslutsskogar. Alla dessa har hjälpt oss bygga en teoretisk vägledning om implementering av maskininlärningsalgoritmer inom betalningsleveransindustrin. Dessa resultat är en robust bekräftelse på fördelarna av dataintensiva modeller även inom sådana komplexa industrier Swedbank Pay är verksamma inom. Implikationerna hjälper vidare att förstärka innovationen och öka intäkterna eftersom de erbjuder en bättre förståelse för deras nuvarande prissättningsmekanism.

Abstract This thesis includes three studies on household preferences for energy goods and services. The first study examines determinants of households’ heating system choices using a choice experiment. The choice sets include six main heating alternatives (district heating, ground heat pump, exhaust air heat pump, solid wood boiler, wood pellet boiler, and electric storage heating) that are described by five attributes (supplementary heating systems, investment costs, operating costs, comfort of use and environmental friendliness). The results imply that hybrid heating appears to be accepted among households. The results also reveal differing preferences for the main heating alternatives and show that they are affected by demographic characteristics. The studied attributes also play a significant role when heating systems are being chosen. The second study is a methodological one that extends the analysis of the first study. The second study explores the effect of perceived choice complexity on the randomness of choices in choice experiments. The study investigates how different self-evaluated factors of choice complexity affect mean scale and scale variance. The findings suggest that perceived choice complexity has a systematic impact on the parameters of econometric models of choice. However, differences exist between alternative self-evaluated complexity-related covariates. The results indicate that individuals who report that answering the choice tasks is more difficult have less deterministic choices. Perceptions of the realism of home heating choice options also affect scale and scale variance. The third study utilizes the choice experiment to analyze households’ willingness to participate in demand side flexibility. The study examines whether individuals are willing to time their electricity usage and heating; whether they are interested in dynamic pricing contracts such as real-time pricing, two-rate tariffs, or power-based tariffs; and how emissions reductions affect their choices. The results indicate that households’ sensitivity to restrictions in electricity usage is much stronger than their sensitivity to restrictions in heating. Households also require compensation to choose real-time pricing over fixed fees. The findings suggest that room may exist for new dynamic electricity distribution contracts, such as power-based tariffs, in the market. Other value-creating elements besides monetary compensation also exist that could incentivize households to offer demand side flexibility because households value power system level reductions in CO2 emissions. / Tiivistelmä Tämä väitöskirja koostuu kolmesta tutkimuksesta, joissa tarkastellaan kotitalouksien preferenssejä energiahyödykkeitä ja -palveluita kohtaan. Ensimmäinen tutkimus keskittyy kotitalouksien lämmitysjärjestelmävalintoihin ja niitä määrittäviin tekijöihin. Tämä tutkimus on tehty valintakoemenetelmällä, jonka valintatilanteet sisältävät kuusi eri päälämmitysjärjestelmävaihtoehtoa (kaukolämpö, maalämpöpumppu, puulämmitys, pellettilämmitys, varaava sähkölämmitys ja poistoilmalämpöpumppu). Päälämmitysjärjestelmiä kuvataan viiden ominaisuuden avulla, jotka ovat tukilämmitysjärjestelmä, investointikustannukset, käyttökustannukset, käyttömukavuus ja ympäristöystävällisyys. Tulosten mukaan kotitalouksien preferenssit päälämmitysjärjestelmävaihtoehtoja kohtaan ovat vaihtelevia. Valintaan vaikuttavat sekä tarkastellut ominaisuudet että kotitalouden demografiset tekijät. Tulokset myös paljastavat, että kotitaloudet suhtautuvat myönteisesti hybridilämmitykseen. Toinen tutkimus on menetelmällinen, missä hyödynnetään ensimmäisen tutkimuksen aineistoa. Tämä tutkimus keskittyy yksilöiden kokeman vastaamisen vaikeuden vaikutuksiin valintakoemenetelmässä. Vastaamisen epätarkkuus tunnistetaan valintakoemenetelmässä skaalan ja skaalavarianssin avulla. Tutkimus tarkastelee, kuinka itsearvioidut vastaamisen vaikeutta mittaavat tekijät vaikuttavat keskimääräiseen skaalaan ja skaalavarianssiin valintojen ekonometrisissa malleissa. Tulosten mukaan koettu vastaamisen vaikeus vaikuttaa systemaattisesti ekonometrisen valintamallin parametreihin. Vastaamisen vaikeutta mittaavien tekijöiden välillä on kuitenkin eroja. Tuloksien perusteella vastaajat, jotka kokevat valintatilanteisiin vastaamisen keskimääräistä vaikeampana, tekevät satunnaisempia valintoja. Myös valintatilanteiden koettu realistisuus vaikuttaa skaalaan ja skaalavarianssiin. Kolmannessa tutkimuksessa arvioidaan kotitalouksien halukkuutta osallistua energian kysyntäjoustoon valintakoemenetelmällä. Tämä tutkimus selvittää ovatko kotitaloudet halukkaitta siirtämään sähkönkulutusta ja lämmitystä, ja kuinka kiinnostuneita he ovat dynaamisista sähkön hinnoittelusopimuksista kuten pörssisähkösopimuksesta, yösähkösopimuksesta tai tehoperusteisesta sopimuksesta. Lisäksi tutkitaan vaikuttavatko järjestelmätason päästövähennykset kotitalouksien valintoihin. Tulosten perusteella kotitaloudet suhtautuvat sähkönkulutuksen rajoituksiin selvästi negatiivisemmin kuin lämmityksen rajoituksiin. Kotitaloudet myös vaativat rahallista korvausta valitakseen pörssisähkösopimuksen kiinteähintaisen sopimuksen sijaan. Tulosten mukaan markkinoilla voisi olla tilaa uudenlaisille sopimustyypeille, kuten tehoperusteiselle vaihtoehdolle. Tulokset osoittavat, että kotitaloudet arvostavat järjestelmätason hiilidioksidipäästövähennyksiä. Täten rahallisen korvauksen lisäksi on olemassa myös muita arvoa luovia tekijöitä lisätä kotitalouksien osallistumista kysyntäjoustoon.

<p>Sequential decision-making is a fundamental class of problem that motivates algorithm designs of online machine learning and reinforcement learning. Arguably, the resulting online algorithms have supported modern online service industries for their data-driven real-time automated decision making. The applications span across different industries, including dynamic pricing (Marketing), recommendation (Advertising), and dosage finding (Clinical Trial). In this dissertation, we contribute fundamental statistical design advances for sequential decision-making algorithms, leaping progress in theory and application of online learning and sequential decision making under uncertainty including online sparse learning, finite-armed bandits, and high-dimensional online decision making. Our work locates at the intersection of decision-making algorithm designs, online statistical machine learning, and operations research, contributing new algorithms, theory, and insights to diverse fields including optimization, statistics, and machine learning.</p> <p><br></p> <p>In part I, we contribute a theoretical framework of continuous risk monitoring for regularized online statistical learning. Such theoretical framework is desirable for modern online service industries on monitoring deployed model's performance of online machine learning task. In the first project (Chapter 1), we develop continuous risk monitoring for the online Lasso procedure and provide an always-valid algorithm for high-dimensional dynamic pricing problems. In the second project (Chapter 2), we develop continuous risk monitoring for online matrix regression and provide new algorithms for rank-constrained online matrix completion problems. Such theoretical advances are due to our elegant interplay between non-asymptotic martingale concentration theory and regularized online statistical machine learning.</p> <p><br></p> <p>In part II, we contribute a bootstrap-based methodology for finite-armed bandit problems, termed Residual Bootstrap exploration. Such a method opens a possibility to design model-agnostic bandit algorithms without problem-adaptive optimism-engineering and instance-specific prior-tuning. In the first project (Chapter 3), we develop residual bootstrap exploration for multi-armed bandit algorithms and shows its easy generalizability to bandit problems with complex or ambiguous reward structure. In the second project (Chapter 4), we develop a theoretical framework for residual bootstrap exploration in linear bandit with fixed action set. Such methodology advances are due to our development of non-asymptotic theory for the bootstrap procedure.</p> <p><br></p> <p>In part III, we contribute application-driven insights on the exploration-exploitation dilemma for high-dimensional online decision-making problems. Such insights help practitioners to implement effective high-dimensional statistics methods to solve online decisionmaking problems. In the first project (Chapter 5), we develop a bandit sampling scheme for online batch high-dimensional decision making, a practical scenario in interactive marketing, and sequential clinical trials. In the second project (Chapter 6), we develop a bandit sampling scheme for federated online high-dimensional decision-making to maintain data decentralization and perform collaborated decisions. These new insights are due to our new bandit sampling design to address application-driven exploration-exploitation trade-offs effectively. </p>

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The understanding of what motivates a customer to pay more for a product or service has al-ways been a fundamental question in business. To the end of answering this question, revenue management is a business practice that revolves around using analytics to predict consumer behavior and willingness-to-pay. It has been a common practice within the commercial airline and hospitality industries for over 30 years, allowing adopters to reach their service capacity with increased profit margins. In this thesis, we investigated the possibility to apply revenue management to the last mile delivery industry, an industry that provides the service of delivering goods from e-commerce companies to the consumer’s front door. To achieve this objective, a revenue management framework was conceived, detailing the interaction between the customer and a dynamic pricing model. The model itself was a product of a machine learning model, intended to segment the customers and predict the willingness-to-pay of each customer segment. The performance of this model was tested through a quantitative study on synthetic buyers, subject to parameters that influence their willingness-to-pay. It was observed that the model was able to distinguish between different types of customers, yielding a pricing policy that increased profits by 7.5% in comparison to fixed price policies. It was concluded that several factors may impact the customer’s willingness-to-pay within the last mile delivery industry. Amongst these, the convenience that the service provides and the disparity between the price of the product and the price of the service were the most notable. However, the magnitude of considering these parameters was never determined. Finally, em-ploying dynamic pricing has the potential to increase the availability of the service, enabling a wider audience to afford the service. / Vad som motiverar en kund att betala mer för en tjänst eller en produkt har länge varit ett centralt koncept inom affärslivet. Intäktsoptimering är en affärspraxis som strävar efter att besvara den frågan, genom att med analytiska verktyg mäta och förutse betalningsviljan hos kunden. Intäktsoptimering har länge varit framträdande inom flyg- och hotellbranschen, där företag som anammat strategin har möjlighets att öka försäljningsvinsten. I detta examensarbete undersöker vi möjligheten att applicera intäktsoptimering på sista milen industrin, en industri som leverar köpta produkten hem till kunden. För att uppnå detta har vi tagit fram ett ramverk för informationsflöden inom intäktsoptimering som beskriver hur kunder interagerar med en dynamisk prissättningsmodell. Denna prissättningsmodell framställs genom maskininlärning med avsikt att segmentera kundbasen, för att sedan förutse betalningsviljan hos varje kundsegment. Modellens prestanda mättes genom en kvantitativ studie på syntetiska kunder som beskrivs av parametrar som påverkar betalningsviljan. Studien påvisade att modellen kunde skilja på betalningsviljan hos olika kunder och resulterade i en genomsnittlig vinstökning på 7.5% i jämförelse med statiska prissättningsmodeller. Det finns mänga olika faktorer som spelar in på kundens betalningsvilja inom sista milen industrin. Bekvämlighet och skillnader i priset på produkten som levereras och tjänsten att leverera produkten är två anmärkningsvärda faktorer. Hur stor inverkan faktorerna som beskrivs i detta examensarbete, har på betalningsviljan, förblev obesvarat. Slutligen uppmärksammades möjligheten att, med hjälp av dynamisk prissättning, öka tillgängligheten av tjänsten då flera kunder kan ha råd med en prissättning som överväger deras betalningsvilja.