Stochastic approximation methods in stochastic optimization

Stochastic approximation methods for stochastic optimization are considered. Reviewed the main methods of stochastic approximation: stochastic quasi-gradient algorithm, Kiefer-Wolfowitz algorithm and adaptive rules for them, simultaneous perturbation stochastic approximation (SPSA) algorithm. Suggested the model and the solution of the retailer's profit optimization problem and considered an application of the SQG-algorithm for the optimization problems with objective functions given in the form of ordinary differential equation.

Numerical Simulation of Stochastic Di erential Equations

Stochastic differential equation (SDE) is a differential equation in which some of the terms and its solution are stochastic processes. SDEs play a central role in modeling physical systems like finance, Biology, Engineering, to mention some. In modeling process, the computation of the trajectories (sample paths) of solutions to SDEs is very important. However, the exact solution to a SDE is generally difficult to obtain due to non-differentiability character of realizations of the Brownian motion. There exist approximation methods of solutions of SDE. The solutions will be continuous stochastic processes that represent diffusive dynamics, a common modeling assumption for financial, Biology, physical, environmental systems. This Masters' thesis is an introduction and survey of numerical solution methods for stochastic differential equations. Standard numerical methods, local linearization methods and filtering methods are well described. We compute the root mean square errors for each method from which we propose a better numerical scheme. Stochastic differential equations can be formulated from a given ordinary differential equations. In this thesis, we describe two kind of formulations: parametric and non-parametric techniques. The formulation is based on epidemiological SEIR model. This methods have a tendency of increasing parameters in the constructed SDEs, hence, it requires more data. We compare the two techniques numerically.

Ison tuuligeneraattorin roottorin ja staattorin rakennetarkastelu

Maailmanlaajuinen ilmastopolitiikka asettaa vaativia tavoitteita hiilidioksidipäästöjen vähentämiselle. Suurin haaste on tuottaa energiaa mahdollisimman alhaisin kustannuksin käyttäen uusiutuvia ja ympäristöä säästäviä energiamuotoja. Tuulivoimasta on tullut nopeimmin kehittyvä sähköntuotantotapa, ja tuuliturbiinien koon kasvun myötä on myös generaattorien koko kasvanut merkittävästi 1990-luvulta lähtien. Generaattorin massiivisuus suoravetoisessa tuuliturbiinin voimansiirrossa vaatii tarkkoja kuormitustarkasteluja, jotta rakenne kestäisi tuuliturbiinin eliniän. Tuuliturbiinin kuormitukset ovat stokastisia ja toisinaan erittäin suuria, mikä vaikeuttaa kuormitusten määrittämistä. Tuulen kuormitusten lisäksi generaattori altistuu eri toimintojen kautta muillekin kuormituksille, ja tästä syystä on otettava huomioon jarrutuksen, dynaamisen tasapainon ja ohjauksen sekä verkkovikojen aiheuttamat rasitukset tuuliturbiinin voimansiirrolle. Edellisten lisäksi työssä on tarkasteltu erilaisia rakenneratkaisuja sekä pyritty kiinnittämään huomio niiden kuormankantokykyyn ja jäykkyyteen sekä generaattorin keventämismahdollisuuksiin verrattuna perinteisiin radiaalivuogeneraattoreihin. Työssä on pyritty selvittämään rakenteen kuormitukset siten, että pystyttäisiin optimoimaan mahdollisimman kevyt rakenne. Optimoinnin kohteena on pinnarakenteisen generaattorin rakenteen massa puolien, puolan kulmien sekä tukirenkaan ja niistä aiheutuvien erilaisten rakenneyhdistelmien suhteen tarkasteltuna.

Demand forecasting process design and methods in medium sized enterprise

The purpose of this thesis was to study the design of demand forecasting processes and management of demand. In literature review were different processes found and forecasting methods and techniques interviewed. Also role of bullwhip effect in supply chain was identified and how to manage it with information sharing operations. In the empirical part of study is at first described current situation and challenges in case company. After that will new way to handle demand introduced with target budget creation and how information sharing with 5 products and a few customers would bring benefits to company. Also the new S&OP process created within this study and organization for it.

The demand-supply balancing process in supply chain management - case mining & metals industry equipment

The aim of this thesis is to search how to match the demand and supply effectively in industrial and project-oriented business environment. The demand-supply balancing process is searched through three different phases: the demand planning and forecasting, synchronization of demand and supply and measurement of the results. The thesis contains a single case study that has been implemented in a company called Outotec. In the case study the demand is planned and forecasted with qualitative (judgmental) forecasting method. The quantitative forecasting methods are searched further to support the demand forecast and long term planning. The sales and operations planning process is used in the synchronization of the demand and supply. The demand forecast is applied in the management of a supply chain of critical unit of elemental analyzer. Different meters on operational and strategic level are proposed for the measurement of performance.

Utilization of AMI-Technology in a Multilayered Demand Response Service Environment

This thesis studied the performance of Advanced metering infrastructure systems in a challenging Demand Response environment. The aim was to find out what kind of challenges and bottlenecks could be met when utilizing AMI-systems in challenging Demand Response tasks. To find out the challenges and bottlenecks, a multilayered demand response service concept was formed. The service consists of seven different market layers which consist of Nordic electricity market and the reserve markets of Fingrid. In the simulations the AMI-systems were benchmarked against these seven market layers. It was found out, that the current generation AMI-systems were capable of delivering Demand Response on the most challenging market layers, when observed from time critical viewpoint. Additionally, it was found out, that to enable wide scale Demand Response there are three major challenges to be acknowledged. The challenges hindering the utilization of wide scale Demand Response were related to poor standardization of the systems in use, possible problems in data connectivity solutions and the current electricity market regulation model.

Accuracy improvement of a demand forecasting model

This research concerns different statistical methods that assist to increase the demand forecasting accuracy of company X’s forecasting model. Current forecasting process was analyzed in details. As a result, graphical scheme of logical algorithm was developed. Based on the analysis of the algorithm and forecasting errors, all the potential directions for model future improvements in context of its accuracy were gathered into the complete list. Three improvement directions were chosen for further practical research, on their basis, three test models were created and verified. Novelty of this work lies in the methodological approach of the original analysis of the model, which identified its critical points, as well as the uniqueness of the developed test models. Results of the study formed the basis of the grant of the Government of St. Petersburg.

Evaluating and improving demand forecast practices for B2B and B2C sectors

Demand forecasting is one of the fundamental managerial tasks. Most companies do not know their future demands, so they have to make plans based on demand forecasts. The literature offers many methods and approaches for producing forecasts. Former literature points out that even though many forecasting methods and approaches are available, selecting a suitable approach and implementing and managing it is a complex cross-functional matter. However, it’s relatively rare that researches are focused on the differences in forecasting between consumer and industrial companies. The aim of this thesis is to investigate the potential of improving demand forecasting practices for B2B and B2C sectors in the global supply chains. Business to business (B2B) sector produces products for other manufacturing companies. On the other hand, consumer (B2C) sector provides goods for individual buyers. Usually industrial sector have a lower number of customers and closer relationships with them. The research questions of this thesis are: 1) What are the main differences and similarities in demand planning between B2B and B2C sectors? 2) How the forecast performance for industrial and consumer companies can be improved? The main methodological approach in this study is design science, where the main objective is to develop tentative solutions to real-life problems. The research data has been collected from a case company. Evaluation and improving in organizing demand forecasting can be found in three interlinked areas: 1) demand planning operational environment, 2) demand forecasting techniques, 3) demand information sharing scenarios. In this research current B2B and B2C demand practices are presented with further comparison between those two sectors. It was found that B2B and B2C sectors have significant differences in demand practices. This research partly filled the theoretical gap in understanding the difference in forecasting in consumer and industrial sectors. In all these areas, examples of managerial problems are described, and approaches for mitigating these problems are outlined.

Potential for synthetic gas in Finnish industry

Finland, other Nordic countries and European Union aim to decarbonize their energy production by 2050. Decarbonization requires large scale implementation of non-emission energy sources, i.e. renewable energy and nuclear power. Stochastic renewable energy sources present a challenge to balance the supply and demand for energy. Energy storages, non-emissions fuels in mobility and industrial processes are required whenever electrification is not possible. Neo-Carbon project studies the decarbonizing the energy production and the role of synthetic gas in it. This thesis studies the industrial processes in steel production, oil refining, cement manufacturing and glass manufacturing, where natural gas is already used or fuel switch to SNG is possible. The technical potential for fuel switching is assessed, and economic potential is necessary after this. All studied processes have potential for fuel switching, but total decarbonization of steel production, oil refining requires implementation of other zero-emission technologies.