Development of a social service in a multi-party network

Tämän pro gradu-tutkimuksen tarkoituksena oli tutkia monen toimijan sosiaalipalvelukehittäjäverkoston toimivuutta ja sen toimivuuteen vaikuttavia tekijöitä. Aihetta lähestyttiin erilaisten teoreettisten kokonaisuuksien kautta, joiden avulla saatiin luotua tutkimukselle pohja. Viitekehys tutkimukselle luotiin yhdistäen erilaisia teoreettisia aihealueita verkostoista, verkostojen johtamisesta ja palveluista. Tutkimuksessa korostuu motivaation, yhteisen, tarpeeseen perustuvan tavoitteen, sitoutumisen ja orkestroinnin merkitys verkostotoiminnassa hyvän lopputuloksen aikaansaamiseksi. Tutkimuksen empiirisessä osuudessa tehty kvalitatiivinen case-tutkimus keskittyy tiettyyn verkostoon, joka on Socomin koordinoimana kehittänyt Kaakkois-Suomen alueelle uudenlaista sosiaalipalvelua liittyen henkilökohtaiseen apuun. Verkosto on monen toimijan verkosto, jonka jäsenet edustavat erilaisia tahoja ja organisaatioita. Tutkimuksen perusteella verkosto on toiminut hyvin ja tehokkaasti ja saanut luotua toimivan sosiaalipalvelun. Verkosto tukee kirjallisuuskatsauksessa löydettyjen tekijöiden, kuten verkosto-orkestroinnin, sitoutumisen ja yhteisen päämäärän, vaikutusta verkoston toimintaan ja lopputulokseen.

Voimalaitoksen käytönaikaisen optimoinnin määrittely

Tässä diplomityössä määritellään biopolttoainetta käyttävän voimalaitoksen käytönaikainen tuotannon optimointimenetelmä. Määrittelytyö liittyy MW Powerin MultiPower CHP –voimalaitoskonseptin jatkokehitysprojektiin. Erilaisten olemassa olevien optimointitapojen joukosta valitaan tarkoitukseen sopiva, laitosmalliin ja kustannusfunktioon perustuva menetelmä, jonka tulokset viedään automaatiojärjestelmään PID-säätimien asetusarvojen muodossa. Prosessin mittaustulosten avulla lasketaan laitoksen energia- ja massataseet, joiden tuloksia käytetään seuraavan optimointihetken lähtötietoina. Optimoinnin kohdefunktio on kustannusfunktio, jonka termit ovat voimalaitoksen käytöstä aiheutuvia tuottoja ja kustannuksia. Prosessia optimoidaan säätimille annetut raja-arvot huomioiden niin, että kokonaiskate maksimoituu. Kun laitokselle kertyy käyttöikää ja historiadataa, voidaan prosessin optimointia nopeuttaa hakemalla tilastollisesti historiadatasta nykytilanteen olosuhteita vastaava hetki. Kyseisen historian hetken katetta verrataan kustannusfunktion optimoinnista saatuun katteeseen. Paremman katteen antavan menetelmän laskemat asetusarvot otetaan käyttöön prosessin ohjausta varten. Mikäli kustannusfunktion laskenta eikä historiadatan perusteella tehty haku anna paranevaa katetta, niiden laskemia asetusarvoja ei oteta käyttöön. Sen sijaan optimia aletaan hakea deterministisellä optimointialgoritmilla, joka hakee nykyhetken ympäristöstä paremman katteen antavia säätimien asetusarvoja. Säätöjärjestelmä on mahdollista toteuttaa myös tulevaisuutta ennustavana. Työn käytännön osuudessa voimalaitosmalli luodaan kahden eri mallinnusohjelman avulla, joista toisella kuvataan kattilan ja toisella voimalaitosprosessin toimintaa. Mallinnuksen tuloksena saatuja prosessiarvoja hyödynnetään lähtötietoina käyttökatteen laskennassa. Kate lasketaan kustannusfunktion perusteella. Tuotoista suurimmat liittyvät sähkön ja lämmön myyntiin sekä tuotantotukeen, ja suurimmat kustannukset liittyvät investoinnin takaisinmaksuun ja polttoaineen ostoon. Kustannusfunktiolle tehdään herkkyystarkastelu, jossa seurataan katteen muutosta prosessin teknisiä arvoja muutettaessa. Tuloksia vertaillaan referenssivoimalaitoksella suoritettujen verifiointimittausten tuloksiin, ja havaitaan, että tulokset eivät ole täysin yhteneviä. Erot johtuvat sekä mallinnuksen puutteista että mittausten lyhyehköistä tarkasteluajoista. Automatisoidun optimointijärjestelmän käytännön toteutusta alustetaan määrittelemällä käyttöön otettava optimointitapa, siihen liittyvät säätöpiirit ja tarvittavat lähtötiedot. Projektia tullaan jatkamaan järjestelmän ohjelmoinnilla, testauksella ja virityksellä todellisessa voimalaitosympäristössä ja myöhemmin ennustavan säädön toteuttamisella.

Qualitative Characteristics and Quantitative Measures of Solution's Reliability in Discrete Optimization: Traditional Analytical Approaches, Innovative Computational Methods and Applicability

The purpose of this thesis is twofold. The first and major part is devoted to sensitivity analysis of various discrete optimization problems while the second part addresses methods applied for calculating measures of solution stability and solving multicriteria discrete optimization problems. Despite numerous approaches to stability analysis of discrete optimization problems two major directions can be single out: quantitative and qualitative. Qualitative sensitivity analysis is conducted for multicriteria discrete optimization problems with minisum, minimax and minimin partial criteria. The main results obtained here are necessary and sufficient conditions for different stability types of optimal solutions (or a set of optimal solutions) of the considered problems. Within the framework of quantitative direction various measures of solution stability are investigated. A formula for a quantitative characteristic called stability radius is obtained for the generalized equilibrium situation invariant to changes of game parameters in the case of the H¨older metric. Quality of the problem solution can also be described in terms of robustness analysis. In this work the concepts of accuracy and robustness tolerances are presented for a strategic game with a finite number of players where initial coefficients (costs) of linear payoff functions are subject to perturbations. Investigation of stability radius also aims to devise methods for its calculation. A new metaheuristic approach is derived for calculation of stability radius of an optimal solution to the shortest path problem. The main advantage of the developed method is that it can be potentially applicable for calculating stability radii of NP-hard problems. The last chapter of the thesis focuses on deriving innovative methods based on interactive optimization approach for solving multicriteria combinatorial optimization problems. The key idea of the proposed approach is to utilize a parameterized achievement scalarizing function for solution calculation and to direct interactive procedure by changing weighting coefficients of this function. In order to illustrate the introduced ideas a decision making process is simulated for three objective median location problem. The concepts, models, and ideas collected and analyzed in this thesis create a good and relevant grounds for developing more complicated and integrated models of postoptimal analysis and solving the most computationally challenging problems related to it.

Multi-axis solutions in packaging machines in Europe

The objective of this thesis was to examine the potential of multi-axis solutions in packaging machines produced in Europe. The definition of a multi-axis solution in this study is a construction that uses a common DC bus power supply for different amplifiers running the axes and the intelligence is centralized into one unit. The cost structure of a packaging machine was gained from an automation research, which divided the machines according to automation categories. The automation categories were then further divided into different sub-components by evaluating the ratio of multi-axis solutions compared to other automation components in packaging machines. A global motion control study was used for further information. With the help of the ratio, an estimation of the potential of multi-axis solutions in each country and packaging machine sector was completed. In addition to the research, a specific questionnaire was sent to five companies to gain information about the present situation and possible trends in packaging machinery. The greatest potential markets are in Germany and Italy, which are also the largest producers of packaging machinery in Europe. The greatest growth in the next few years will be seen in Turkey where the annual growth rate equals the general machinery production rate in Asia. The greatest market potential of the Nordic countries is found in Sweden in 35th position on the list. According to the interviews, motion control products in packaging machines will retain their current power levels, as well as the number of axes in the future. Integrated machine safety features together with a universal programming language are the desired attributes of the future. Unlike generally in industry, the energy saving objectives are and will remain insignificant in the packaging industry.

Innovation in a Non-Formal Adult Education Organisation – Multi-Case Study in Four Adult Education Centres

More than ever, education organisations are experiencing the need to develop new services and processes to satisfy expanding and changing customer needs and to adapt to the environmental changes and continually tightening economic situation. Innovation has been found in many studies to have a crucial role in the success of an organisation, both in the private and public sectors, in formal education and in manufacturing and services alike. However, studies concerning innovation in non-formal adult education organisations, such as adult education centres (AECs) in Finland, are still lacking. This study investigates innovation in the non-formal adult education organisation context from the perspective of organisational culture types and social networks. The objective is to determine the significant characteristics of an innovative non-formal adult education organisation. The analysis is based on data from interviews with the principals and fulltime staff of four case AECs. Before the case study, a pre-study phase is accomplished in order to obtain a preliminary understanding of innovation at AECs. The research found strong support for the need of innovation in AECs. Innovation is basically needed to accomplish the AEC system’s primary mission mentioned in the ACT on Liberal Adult Education. In addition, innovation is regarded vital to institutes and may prevent their decline. It helps the institutes to be more attractive, to enter new market, to increase customer satisfaction and to be on the cutting edge. Innovation is also seen as a solution to the shortage of resources. Innovative AECs search actively for additional resources for development work through project funding and subsidies, cooperation networks and creating a conversational and joyful atmosphere in the institute. The findings also suggest that the culture type that supports innovation at AECs is multidimensional, with an emphasis on the clan and adhocratic culture types and such values as: dynamism, future orientation, acquiring new resources, mistake tolerance, openness, flexibility, customer orientation, a risk-taking attitude, and community spirit. Active and creative internal and external cooperation also promote innovation at AECs. This study also suggests that the behaviour of a principal is crucial. The way he or she shows appreciation the staff, encouragement and support to the staff and his or her approachability and concrete participation in innovation activities have a strong effect on innovation attitudes and activities in AECs.

Methodology for wind turbine blade geometry optimization

Nowadays, the upwind three bladed horizontal axis wind turbine is the leading player on the market. It has been found to be the best industrial compromise in the range of different turbine constructions. The current wind industry innovation is conducted in the development of individual turbine components. The blade constitutes 20-25% of the overall turbine budget. Its optimal operation in particular local economic and wind conditions is worth investigating. The blade geometry, namely the chord, twist and airfoil type distributions along the span, responds to the output measures of the blade performance. Therefore, the optimal wind blade geometry can improve the overall turbine performance. The objectives of the dissertation are focused on the development of a methodology and specific tool for the investigation of possible existing wind blade geometry adjustments. The novelty of the methodology presented in the thesis is the multiobjective perspective on wind blade geometry optimization, particularly taking simultaneously into account the local wind conditions and the issue of aerodynamic noise emissions. The presented optimization objective approach has not been investigated previously for the implementation in wind blade design. The possibilities to use different theories for the analysis and search procedures are investigated and sufficient arguments derived for the usage of proposed theories. The tool is used for the test optimization of a particular wind turbine blade. The sensitivity analysis shows the dependence of the outputs on the provided inputs, as well as its relative and absolute divergences and instabilities. The pros and cons of the proposed technique are seen from the practical implementation, which is documented in the results, analysis and conclusion sections.

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.

Production planning and optimization of a pulp mill: introduction of the optimisation based scheduling software for pulp industry

The objective of this project was to introduce a new software product to pulp industry, a new market for case company. An optimization based scheduling tool has been developed to allow pulp operations to better control their production processes and improve both production efficiency and stability. Both the work here and earlier research indicates that there is a potential for savings around 1-5%. All the supporting data is available today coming from distributed control systems, data historians and other existing sources. The pulp mill model together with the scheduler, allows what-if analyses of the impacts and timely feasibility of various external actions such as planned maintenance of any particular mill operation. The visibility gained from the model proves also to be a real benefit. The aim is to satisfy demand and gain extra profit, while achieving the required customer service level. Research effort has been put both in understanding the minimum features needed to satisfy the scheduling requirements in the industry and the overall existence of the market. A qualitative study was constructed to both identify competitive situation and the requirements vs. gaps on the market. It becomes clear that there is no such system on the marketplace today and also that there is room to improve target market overall process efficiency through such planning tool. This thesis also provides better overall understanding of the different processes in this particular industry for the case company.

Efficient Optimization Algorithms for Nonlinear Data Analysis

Identification of low-dimensional structures and main sources of variation from multivariate data are fundamental tasks in data analysis. Many methods aimed at these tasks involve solution of an optimization problem. Thus, the objective of this thesis is to develop computationally efficient and theoretically justified methods for solving such problems. Most of the thesis is based on a statistical model, where ridges of the density estimated from the data are considered as relevant features. Finding ridges, that are generalized maxima, necessitates development of advanced optimization methods. An efficient and convergent trust region Newton method for projecting a point onto a ridge of the underlying density is developed for this purpose. The method is utilized in a differential equation-based approach for tracing ridges and computing projection coordinates along them. The density estimation is done nonparametrically by using Gaussian kernels. This allows application of ridge-based methods with only mild assumptions on the underlying structure of the data. The statistical model and the ridge finding methods are adapted to two different applications. The first one is extraction of curvilinear structures from noisy data mixed with background clutter. The second one is a novel nonlinear generalization of principal component analysis (PCA) and its extension to time series data. The methods have a wide range of potential applications, where most of the earlier approaches are inadequate. Examples include identification of faults from seismic data and identification of filaments from cosmological data. Applicability of the nonlinear PCA to climate analysis and reconstruction of periodic patterns from noisy time series data are also demonstrated. Other contributions of the thesis include development of an efficient semidefinite optimization method for embedding graphs into the Euclidean space. The method produces structure-preserving embeddings that maximize interpoint distances. It is primarily developed for dimensionality reduction, but has also potential applications in graph theory and various areas of physics, chemistry and engineering. Asymptotic behaviour of ridges and maxima of Gaussian kernel densities is also investigated when the kernel bandwidth approaches infinity. The results are applied to the nonlinear PCA and to finding significant maxima of such densities, which is a typical problem in visual object tracking.

Distribution network design and optimization

The objective of this thesis is to examine distribution network designs and modeling practices and create a framework to identify best possible distribution network structure for the case company. The main research question therefore is: How to optimize case company’s distribution network in terms of customer needs and costs? Theory chapters introduce the basic building blocks of the distribution network design and needed calculation methods and models. Framework for the distribution network projects was created based on the theory and the case study was carried out by following the defined framework. Distribution network calculations were based on the company’s sales plan for the years 2014 - 2020. Main conclusions and recommendations were that the new Asian business strategy requires high investments in logistics and the first step is to open new satellite DC in China as soon as possible to support sales and second possible step is to open regional DC in Asia within 2 - 4 years.

Differential evolution based classification with pool of distances and aggregation operators

The objective of this thesis is to develop and generalize further the differential evolution based data classification method. For many years, evolutionary algorithms have been successfully applied to many classification tasks. Evolution algorithms are population based, stochastic search algorithms that mimic natural selection and genetics. Differential evolution is an evolutionary algorithm that has gained popularity because of its simplicity and good observed performance. In this thesis a differential evolution classifier with pool of distances is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, to determine the optimal values for all free parameters of the classifier model during the training phase of the classifier. The differential evolution classifier applies the individually optimized distance measure for each new data set to be classified is generalized to cover a pool of distances. Instead of optimizing a single distance measure for the given data set, the selection of the optimal distance measure from a predefined pool of alternative measures is attempted systematically and automatically. Furthermore, instead of only selecting the optimal distance measure from a set of alternatives, an attempt is made to optimize the values of the possible control parameters related with the selected distance measure. Specifically, a pool of alternative distance measures is first created and then the differential evolution algorithm is applied to select the optimal distance measure that yields the highest classification accuracy with the current data. After determining the optimal distance measures for the given data set together with their optimal parameters, all determined distance measures are aggregated to form a single total distance measure. The total distance measure is applied to the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; a sample belongs to the class represented by the nearest prototype vector when measured with the optimized total distance measure. During the training process the differential evolution algorithm determines the optimal class vectors, selects optimal distance metrics, and determines the optimal values for the free parameters of each selected distance measure. The results obtained with the above method confirm that the choice of distance measure is one of the most crucial factors for obtaining higher classification accuracy. The results also demonstrate that it is possible to build a classifier that is able to select the optimal distance measure for the given data set automatically and systematically. After finding optimal distance measures together with optimal parameters from the particular distance measure results are then aggregated to form a total distance, which will be used to form the deviation between the class vectors and samples and thus classify the samples. This thesis also discusses two types of aggregation operators, namely, ordered weighted averaging (OWA) based multi-distances and generalized ordered weighted averaging (GOWA). These aggregation operators were applied in this work to the aggregation of the normalized distance values. The results demonstrate that a proper combination of aggregation operator and weight generation scheme play an important role in obtaining good classification accuracy. The main outcomes of the work are the six new generalized versions of previous method called differential evolution classifier. All these DE classifier demonstrated good results in the classification tasks.

Utilization of multi-branding in business to business markets

Case company utilizes multi-branding strategy (or house of brands strategy) in its product portfolio. In practice the company has multiple brands – one main brand and four acquired brands – which all utilize one single product platform. The objective of this research is to analyze case company’s multi-branding strategy and its benefits and challenges. Moreover, the purpose is to clarify that how could a company in B2B markets utilize multi-branding strategy more efficiently and profitably. The theoretical part of this thesis consists of aspects of branding strategies; different brand name architectures, benefits and challenges of different strategies and different ways of utilize branding strategies in mergers and acquisitions. The empirical part, on the other hand, includes the description of the case company’s branding strategy and the employees’ perspective on the benefits and challenges of multi-branding strategy, and how to utilize it more efficiently and profitably. This study shows, that the major benefits of utilizing multi-branding are lower production costs, ability to reach wider market coverage, possibility to utilize common sales tools, synergies in R&D and shared resources. On the other hand, the major challenges are lack of product differentiation, internal competition, branding issues in production and deliveries, pricing issues and conflicts, and compromises in product compatibility and suitability. Based on the results, several ways to utilize multi-branding strategy more efficiently and profitably were found; by putting more effort on brand image and product differentiation, by having more co-operation among the brands and by focusing on more precise customer and market segmentation.

Distributed energy resources in an electricity retailer’s short-term profit optimization

Liberalization of electricity markets has resulted in a competed Nordic electricity market, in which electricity retailers play a key role as electricity suppliers, market intermediaries, and service providers. Although these roles may remain unchanged in the near future, the retailers’ operation may change fundamentally as a result of the emerging smart grid environment. Especially the increasing amount of distributed energy resources (DER), and improving opportunities for their control, are reshaping the operating environment of the retailers. This requires that the retailers’ operation models are developed to match the operating environment, in which the active use of DER plays a major role. Electricity retailers have a clientele, and they operate actively in the electricity markets, which makes them a natural market party to offer new services for end-users aiming at an efficient and market-based use of DER. From the retailer’s point of view, the active use of DER can provide means to adapt the operation to meet the challenges posed by the smart grid environment, and to pursue the ultimate objective of the retailer, which is to maximize the profit of operation. This doctoral dissertation introduces a methodology for the comprehensive use of DER in an electricity retailer’s short-term profit optimization that covers operation in a variety of marketplaces including day-ahead, intra-day, and reserve markets. The analysis results provide data of the key profit-making opportunities and the risks associated with different types of DER use. Therefore, the methodology may serve as an efficient tool for an experienced operator in the planning of the optimal market-based DER use. The key contributions of this doctoral dissertation lie in the analysis and development of the model that allows the retailer to benefit from profit-making opportunities brought by the use of DER in different marketplaces, but also to manage the major risks involved in the active use of DER. In addition, the dissertation introduces an analysis of the economic potential of DER control actions in different marketplaces including the day-ahead Elspot market, balancing power market, and the hourly market of Frequency Containment Reserve for Disturbances (FCR-D).