Decisions made and actions taken by individuals in the operations function impact the formation of a company’s manufacturing strategy (MS). Therefore, it is important that the MS is understood and agreed on by all employees, that is, strategic consensus among the individuals in the operations function is essential. This research contributes to the current body of knowledge by including a workers’ perspective on MS formation. It is the workers on the shop floor who bring the MS to life in the actual operations through their daily decisions and actions. The MS falls short if the priorities outlined do not materialise in practice as intended. The purpose of this research is to investigate how the individuals in the operations function perceive the MS in order to understand how these individuals are involved in the MS formation. The research is based on five studies, differing by evidence, as follows: one theoretical, three qualitative in the setting of small and medium-sized enterprises (SMEs), and one quantitative at a large company. Based on the findings presented in the six appended papers, the results show that empirically and conceptually, workers have been overlooked or given a passive role in the MS formation. Empirically, it is seen that workers and managers do not have a shared understanding of the underlying reasons for strategic priorities; hence, the level of strategic consensus is low. Furthermore, the level of strategic consensus varies among the different MS dimensions depending on their organisational level. Moreover, the empirical findings reveal that internal contextual factors influence the individuals’ perceptions of the MS and the possibilities for strategic consensus. Regarding the external context, the results show that major customers’ strategies influence the subcontractor SMEs’ MS formation. The usage of means of communication in the operations function has also shown to be of importance for how the MS is perceived. Conceptually, the findings indicate that the MS literature tends to treat individuals in the operations function in a deterministic manner; individuals on the shop floor are regarded as manufacturing resources. To ensure a successful MS formation process, where the patterns of the decisions made by the individuals in the operations function forms the MS, the view on human nature within the MS requires a more voluntaristic approach. This research suggests to view the MS formation as an iterative “patterning process” which builds on a reciprocal relationship between workers and managers. The introduction of the patterning process contributes to the research on MS formation by explaining the perception range within the hierarchical levels, by re-defining the hierarchical levels included in the MS formation and by detailing the activities in the MS formation.

The decentralized consensus control of a formation of rigid-body spacecraft is studied in the framework of geometric mechanics while accounting for a constant communication time delay between spacecraft. The relative position and attitude (relative pose) are represented on the Lie group SE(3) and the communication topology is modeled as a digraph. The consensus problem is converted into a local stabilization problem of the error dynamics associated with the Lie algebra se(3) in the form of linear time-invariant delay differential equations with a single discrete delay in the case of a circular orbit, whereas it is in the form of linear time-periodic delay differential equations in the case of an elliptic orbit, in which the stability may be assessed using infinite-dimensional Floquet theory. The proposed technique is applied to the consensus control of four spacecraft in the vicinity of a Molniya orbit.

published_or_final_version / Public Administration / Master / Master of Public Administration

Protein structure prediction has been a fundamental challenge in the biological field. In this post-genomic era, the need for automated protein structure prediction has never been more evident and researchers are now focusing on developing computational techniques to predict three-dimensional structures with high throughput. Consensus-based protein structure prediction methods are state-of-the-art in automatic protein structure prediction. A consensus-based server combines the outputs of several individual servers and tends to generate better predictions than any individual server. Consensus-based methods have proved to be successful in recent CASP (Critical Assessment of Structure Prediction). In this thesis, a Support Vector Machine (SVM) regression-based consensus method is proposed for protein fold recognition, a key component for high throughput protein structure prediction and protein function annotation. The SVM first extracts the features of a structural model by comparing the model to the other models produced by all the individual servers. Then, the SVM predicts the quality of each model. The experimental results from several LiveBench data sets confirm that our proposed consensus method, SVM regression, consistently performs better than any individual server. Based on this method, we developed a meta server, the Alignment by Consensus Estimation (ACE).

The identification of high quality journals often serves as a basis for the assessment of research contributions. In this context rankings have become an increasingly popular vehicle to decide upon incentives for researchers, promotions, tenure or even library budgets. These rankings are typically based on the judgments of peers or domain experts or scientometric methods (e.g., citation frequencies, acceptance rates). Depending on which (combination) of these ranking approaches is followed, the outcome leads to more or less diverging results. This paper addresses the issue on how to construct suitable aggregate (subsets) of these rankings. We present an optimization based consensus ranking approach and apply the proposed method to a subset of marketing-related journals from the Harzing Journal Quality List. Our results show that even though journals are not uniformly ranked it is possible to derive a consensus ranking with considerably high agreement among the individual rankings. In addition, we explore regional differences in consensus rankings. / Series: Research Report Series / Department of Statistics and Mathematics

Bookmakers odds are an easily available source of ``prospective" information that is thus often employed for forecasting the outcome of sports events. To investigate the statistical properties of bookmakers odds from a variety of bookmakers for a number of different potential outcomes of a sports event, a class of mixed-effects models is explored, providing information about both consensus and (dis)agreement across bookmakers. In an empirical study for the UEFA Champions League, the most prestigious football club competition in Europe, model selection yields a simple and intuitive model with team-specific means for capturing consensus and team-specific standard deviations reflecting agreement across bookmakers. The resulting consensus forecast performs well in practice, exhibiting high correlation with the actual tournament outcome. Furthermore, the teams' agreement can be shown to be strongly correlated with the predicted consensus and can thus be incorporated in a more parsimonious model for agreement while preserving the same consensus fit. / Series: Research Report Series / Department of Statistics and Mathematics

Modern technologies have resulted in the production of numerous high-throughput biological datasets. However, the pace of development of capable computational methods does not cope with the pace of generation of new high-throughput datasets. Amongst the most popular biological high-throughput datasets are gene expression datasets (e.g. microarray datasets). This work targets this aspect by proposing a suite of computational methods which can analyse multiple gene expression datasets collectively. The focal method in this suite is the unification of clustering results from multiple datasets using external specifications (UNCLES). This method applies clustering to multiple heterogeneous datasets which measure the expression of the same set of genes separately and then combines the resulting partitions in accordance to one of two types of external specifications; type A identifies the subsets of genes that are consistently co-expressed in all of the given datasets while type B identifies the subsets of genes that are consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets. This contributes to the types of questions which can addressed by computational methods because existing clustering, consensus clustering, and biclustering methods are inapplicable to address the aforementioned objectives. Moreover, in order to assist in setting some of the parameters required by UNCLES, the M-N scatter plots technique is proposed. These methods, and less mature versions of them, have been validated and applied to numerous real datasets from the biological contexts of budding yeast, bacteria, human red blood cells, and malaria. While collaborating with biologists, these applications have led to various biological insights. In yeast, the role of the poorly-understood gene CMR1 in the yeast cell-cycle has been further elucidated. Also, a novel subset of poorly understood yeast genes has been discovered with an expression profile consistently negatively correlated with the well-known ribosome biogenesis genes. Bacterial data analysis has identified two clusters of negatively correlated genes. Analysis of data from human red blood cells has produced some hypotheses regarding the regulation of the pathways producing such cells. On the other hand, malarial data analysis is still at a preliminary stage. Taken together, this thesis provides an original integrative suite of computational methods which scrutinise multiple gene expression datasets collectively to address previously unresolved questions, and provides the results and findings of many applications of these methods to real biological datasets from multiple contexts.

This June one of the biggest and most popular sports tournaments will take place in Austria and Switzerland, the European soccer championship 2008 (UEFA EURO 2008). Therefore millions of soccer fans in Europe and throughout the world are asking themselves: "Who is going to win the EURO 2008?" Many people, including sports experts and former players, give their guesses and expectations in the media, but there is also a group with financial incentives, like some economists who expect economical increases for the country of the winning team and bookmakers and their customers who directly make money with their beliefs. Some predictions are only guesses, but other predictions are based on quantitative methods, such as the studies of UBS Wealth Management Research Switzerland and the Raiffeisen Zentralbank. In this report we will introduce a new method for predicting the winner. Whereas other prediction methods are based on historical data, e.g., the Elo rating, or the FIFA/Coca Cola World rating, our method is based on current expectations, the bookmakers odds for winning the championship. In particular we use the odds for winning the championship for each of the 16 teams of 45 international bookmakers. By interpreting these odds as rating of the expected strength of the teams by the bookmakers, we derive a consensus rating by modelling the log-odds using a random-effects model with a team-specific random effect and a bookmaker-specific fixed effect. The consensus rating of a team can be used as an estimator for the unknown "true" strength of a team. Our method predicts team Germany with a probability of about 18.7% as the EURO 2008 winner. We predict also that the teams playing the final will be Germany and Spain with a probability of 13.9%, where Germany will win with a probability of 55%. In our study, Italy, the favorite according to the current FIFA/Coca Cola World ranking and Elo ranking, has a much lower probability than these teams to win the tournament: only 10.6%. The defending champion Greece has low chances to win the title again: about 3.4%. Furthermore, the expected performance of the host countries, Austria and Switzerland, is much better in the bookmakers consensus than in the retrospective Elo and FIFA/Coca Cola World ratings, i.e., indicating an (expected) home court advantage. Despite the associated increase in the winning probabilities, both teams have rather poor chances to win the tournament with probabilities of 1.3% and 4.0%, respectively. In a group effect study we investigate how much the classification into the four groups (A-D) affects the chance for a team to win the championship. / Series: Research Report Series / Department of Statistics and Mathematics

Redes são representações poderosas para muitos sistemas complexos, onde vértices representam elementos do sistema e arestas representam conexões entre eles. Redes Complexas podem ser definidas como grafos de grande escala que possuem distribuição não trivial de conexões. Um tópico importante em redes complexas é a detecção de comunidades. Embora a detecção de comunidades tenha revelado bons resultados na análise de agrupamento de dados com grupos de diversos formatos, existem ainda algumas dificuldades na representação em rede de um conjunto de dados. Outro tópico recente é a caracterização de simplicidade em redes complexas. Existem poucos trabalhos nessa área, no entanto, o tema tem muita relevância, pois permite analisar a simplicidade da estrutura de conexões de uma região de vértices, ou de toda a rede. Além disso, mediante a análise de simplicidade de redes dinâmicas no tempo, é possível conhecer como vem se comportando a evolução da rede em termos de simplicidade. Considerando a rede como um sistema dinâmico de agentes acoplados, foi proposto neste trabalho uma medida de distância baseada no tempo de consenso na presença de um líder em uma rede acoplada. Utilizando essa medida de distância, foi proposto um método de detecção de comunidades para análise de agrupamento de dados, e um método de análise de simplicidade em redes complexas. Além disso, foi proposto uma técnica de construção de redes esparsas para agrupamento de dados. Os métodos têm sido testados com dados artificiais e reais, obtendo resultados promissores / Networks are powerful representations for many complex systems, where nodes represent elements of the system and edges represent connections between them. Complex networks can be defined as graphs with no trivial distribution of connections. An important topic in complex networks is the community detection. Although the community detection have reported good results in the data clustering analysis with groups of different formats, there are still some dificulties in the representation of a data set as a network. Another recent topic is the characterization of simplicity in complex networks. There are few studies reported in this area, however, the topic has much relevance, since it allows analyzing the simplicity of the structure of connections between nodes of a region or connections of the entire network. Moreover, by analyzing simplicity of dynamic networks in time, it is possible to know the behavior in the network evolution in terms of simplicity. Considering the network as a coupled dynamic system of agents, we proposed a distance measure based on the consensus time in the presence of a leader in a coupled network. Using this distance measure, we proposed a method for detecting communities to analyze data clustering, and a method for simplicity analysis in complex networks. Furthermore, we propose a technique to build sparse networks for data clustering. The methods have been tested with artificial and real data, obtaining promising results

Yes / Although the eurozone sovereign debt crisis took many by surprise following the Global Financial Crisis induced Great Recession, this chapter argues that this was an accident waiting to happen with unjustified emphasis placed upon unproven rules and institutions derived from contemporary neoliberal macroeconomic thinking. First, recent developments in macroeconomic are discussed and evaluated in terms of the so-called New Consensus Macroeconomics (NCM) that forms the current mainstream macroeconomic model comprising a blend of New Classical and New Keynesian theories is through adopting the rational behaviour hypothesis and supply-side-determined long-term equilibrium of output. A particular feature of these ideas is the inclusion of rules and institutions that are perceived to result in time consistent policymaking through essentially binding politicians from undertaking in non-optimal behaviour for either opportunistic, partisan or non-rational expectations reasons. Second, in addition to the general backdrop of macroeconomics the chapter considers the notion of a monetary union between countries under the rubric of both exogenous and endogenous Optimum Currency Area (OCA) theory. This combination of theoretical propositions form the bedrock of the eurozone where the TEU convergence criteria and SGP form the rules, while the European Central Bank is the key institution tasked with delivering low and stable price inflation. However, although these notions have become the staple diet of a generation of mainstream economists they comprehensively failed to insulate the eurozone from its sovereign debt crisis. / Full text of this chapter will be released for public view at the end of the publisher embargo on 1 Nov 2019.