### Browsing by Author "Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland"

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Item Climate impacts of bioenergy from forest harvest residues(Aalto University, 2015) Repo, Anna; Liski, Jari, Research Prof., Finnish Environment Institute SYKE, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandClimate change mitigation requires substantial cuts in greenhouse gas emissions in the next decades. One option to reduce these emissions is to replace fossil fuels with low-carbon alternatives. Bioenergy from forest harvest residues has been considered as a carbon neutral source of energy, and therefore it has been regarded as an effective means to reduce the emissions. However, an increase in the extraction of forest harvest residues decreases the carbon stock, and the carbon sink capacity of forests. This effect can lessen the greenhouse gas emission savings and undermine the climate change mitigation potential of this bioenergy source. This dissertation examines the climate impacts of bioenergy produced from forest harvest residues. In this dissertation, an approach was developed to quantify the greenhouse gas emissions and the consequent warming climate impact of bioenergy from forest harvest residues. In addition, this dissertation suggests cost-effective ways to compensate for the carbon loss resulting from residue harvesting, and thus improve the climate impacts of this form of bioenergy. The dissertation illustrates the importance of accounting for reductions in the forest carbon stock in order to estimate the efficiency of bioenergy in reducing CO2 emissions reliably. The findings of this dissertation have implications for renewable energy and climate policies, and forest management. The results presented provide guidance on how to choose and plan bioenergy production practices that deliver the largest climate benefits. The approaches presented in this dissertation can be applied in the development of new forest management, which maximizes climate benefits of bioenergy from forest harvest residues with a low cost to the forest owner and the end-user of bioenergy.Item Computational models for adversarial risk analysis and probabilistic scenario planning(Aalto University, 2023) Roponen, Juho; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandPeople need to make decisions under uncertainty. Both in corporate and public governance, in addition to uncertainty, the decisions can have high costs and far-reaching consequences. Thus, choosing a good decision alternative, or at least avoiding the inferior ones, is crucial. Two sources of uncertainty are especially prevalent in these decision problems: human activity and long planning horizons. In this dissertation, methods for addressing uncertainties arising from both these sources are developed. By quantifying these uncertainties as probability distributions and preferences over outcomes as utility functions, a well-defined mathematical decision problem can be constructed and then solved using optimization techniques. First, methods for adversarial risk analysis are developed to model the decision processes of adversarial actors who deliberately try to advance their own interests. The proposed methods facilitate systematic probabilistic analyses with limited knowledge about the adversary's preferences and their available information. This can be especially useful when the exact way the adversary analyzes the situation is difficult to assess or when their goals are deliberately hidden, as is often the case when analyzing military combat or security problems. The dissertation also demonstrates how combat modeling and simulation tools can be applied in adversarial risk analysis. This expands the types of analyses these tools can be used for, making it possible to answer questions such as, how the adversary's actions are impacted by changing circumstances, or how the outcomes of individual battles impact the larger strategic situation. Second, a new probabilistic cross-impact analysis model is developed to quantify uncertainties associated with future scenarios based on information elicited from subject matter experts. Two different computational approaches are presented for analyzing the elicited cross-impact statements. One takes information about upper and lower bounds on probabilities and then calculates upper and lower bounds on system risk or utility. The other takes the best estimates about probabilities of specific uncertainty factors and their interactions and constructs a joint probability distribution and a Bayesian network. These approaches can be useful when probability information based on statistics or simulations is not available, for example when results need to be produced quickly or the uncertainties are associated with relatively far-off future events or human activity.Item Decision models for managing demand and supply uncertainties in supply networks(Aalto University, 2014) Käki, Anssi; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Talluri, Srinivas, Prof., Michigan State University, USA; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandCompanies can use mathematical models to improve their decisions under uncertainty. This Dissertation focuses on sourcing and supply management decisions under i) uncertain demand of products or components and ii) uncertain capability of suppliers to deliver high-quality materials and services. Modern planning systems help automatize and optimize decision making in these areas, but most these systems are not good at accounting for uncertainties. However, in many industries, the effective management of demand and supply uncertainties is an important source of competitive advantage. Thus, there is a need for decision models that help managers analyze the impacts of uncertainties. The Dissertation develops methods based on stochastic optimization where both continuousand discrete (scenario-based) probability distributions are used to model demand and supplyuncertainties. Particular attention is given to the qualitative characteristics of distributionsand interdependencies between uncertainties. In addition, a static methodology for assessingdisruption risks in complex supply networks is presented. The Dissertation illustrates how the neglect of uncertainties can lead to sub-optimal decisionrecommendations, and, on the other hand, how a decision maker can better utilize decisionmodels by modeling the relevant uncertainties appropriately. The theoretical part iscomplemented with experimental results which show that subjects have significant difficultiesin making simple procurement decisions in the presence of demand and supply uncertainties,and that decision support tools can significantly improve their decision making in this area. The careful modeling of uncertainties yields robust decision recommendations that perform well in most or all uncertainty scenarios. By using the methodologies presented in Dissertation, managers can better manage the uncertainties in customer demand and suppliers' performance and material availability. This increases the competitiveness and capability to manage risks in an uncertain business environment.Item Knowledge creation in foresight: A practice- and systems-oriented view(Aalto University, 2015) Dufva, Mikko; Ahlqvist, Toni, Prof., University of Oulu, Finland (Until 30 November 2015 Principal Scientist, VTT Technical Research Centre of Finland Ltd.); Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandThis dissertation studies the creation of futures knowledge in the practice of foresight. By futures knowledge I mean the perceptions about futures expressed by various stakeholders. Foresight is commonly used for anticipating future developments, scoping alternative futures and creating present actions based on futures knowledge. It is usually depicted as a process, where a group of experts and other stakeholders gather and produce knowledge about the future. This process view is problematic, because it does not usually consider the influence of the events and processes taking place outside a separate foresight process. The foresight process is often viewed as a strategic exercise disconnected from the everyday operations of organisations. Despite the wide use of foresight, the creation of futures knowledge has not received much attention in research on foresight. Instead, the focus has been on the production of outcomes, such as scenarios, roadmaps and visions. In this dissertation, I present a systems view of foresight and study futures knowledge creation from a systems perspective. The theories and approaches on innovation systems, complex adaptive systems and foresight form the theoretical basis. My research methods are based on grounded theory and the research material consists of five foresight projects. The main results include futures knowledge typology, elements of a foresight system, futures knowledge as a network of concepts and a multi-layered foresight framework. Based on these results, I present two complementary views of futures knowledge creation. First, I argue that futures knowledge is created through the conversions between different types of knowledge. Second, futures knowledge is created gradually through the interaction between humans, dependent on the nature of the interaction, and can be seen as the shaping of the network of concepts. The main theoretical contribution of this dissertation is the further elaboration of the systems view of foresight. This includes the elements of a foresight system, the futures knowledge typology and the multi-layered foresight. These can be applied in the study of foresight processes to identify and analyse different ways by which the processes create futures knowledge and support the formation of strategy. The main practical implication of the systems view to foresight is the shift from seeing foresight projects as separate to perceiving them as part of an interconnected whole. In order to enhance the creation of futures knowledge, these processes need to be flexible and enable intensive and broad participation among participants. In addition, futures knowledge should be seen more as a network of perceptions about alternative futures than separate outcomes of foresight projects.Item Model-based decision processes for agenda building and project funding(Aalto University, 2014) Vilkkumaa, Eeva; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Liesiö, Juuso, Dr., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Systeemianalyysin laboratorio; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandEssentially all organizations need to recognize relevant future developments in their operational environment as a backdrop for building strategic priorities which are typically implemented by choosing corresponding actions (such as R&D projects). These interlinked processes – most notably horizon scanning, strategic priority-setting and project selection – can all be framed as decision problems in which a subset or portfolio of alternatives is to be selected subject to limited resources and other relevant constraints. They can therefore be approached with methods of portfolio decision analysis (PDA) in order to maximize the value that the selected portfolio can be expected to yield and also to improve the transparency and quality of decision processes. This Dissertation develops PDA methods to support the above decision processes, particularly in contexts where there are significant uncertainties. These methods capture uncertainties through set inclusion of feasible parameters and probability distributions. The methods accommodate the possibly conflicting preferences of multiple decision-makers, and they help identify portfolios that are resilient across a range of scenarios about the future. They also help mitigate so-called post-decision disappointment, which results from the fact that those projects whose values have been overestimated are more likely to be selected. The methods in this Dissertation can also be used to develop optimal project funding policies which maximize the average value of the selected project portfolio or the number of those projects whose values are exceptionally high. They also guide the reduction of uncertainties by indicating about which projects it is optimal to acquire additional value estimates such that the resulting increase in the value of the portfolio exceeds the costs of acquiring such estimates.Item Model-based efficiency analyses of healthcare delivery(Aalto University, 2018) Hynninen, Yrjänä; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Vilkkumaa, Eeva, Prof., Aalto University, Department of Information and Service Management, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandIn the years to come, healthcare organizations are challenged to deliver care to more patients, of higher quality, and with scarce financial and human resources. To improve the value of healthcare delivery, allocating resources efficiently is paramount. In support of allocation decisions, there is an increasing amount and variety of patient-related information available from, for example, clinical tests and biomarkers. This information provides substantial possibilities of improving healthcare but, nevertheless, its full exploitation requires advanced methods. This Dissertation develops and applies mathematical models to support the efficient use and allocation of resources in healthcare. The models help assess the efficiency of healthcare systems and thus identify best practices for learning. Furthermore, the models can be used to identify efficient testing and intervention strategies. These models can support, for instance, the benchmarking of healthcare systems, clinicians' decision making, policy making, and decisions on the acquisition or price setting of testing or treatment technologies. The Dissertation provides evidence in support of the claim that systematic methods of efficiency and decision analysis help improve the practices of healthcare. For example, the Dissertation demonstrates that the use of decision-analytic modeling and optimization methods is useful when identifying such prevention, detection, and treatment actions which are, on the one hand, targeted based on patients' personal information, and, on the other hand, efficient on the population-level examination. Also, making the impact of value judgments explicit related to healthcare resource allocation decisions is important and possible with advanced models and methods.Item Models and algorithms for vehicle routing, resource allocation, and multi-stage decision-making under uncertainty(Aalto University, 2021) Andelmin, Juho; Bartolini, Enrico, Dr., RWTH Aachen University, Germany; Oliveira, Fabricio, Asst. Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Liesiö, Juuso, Asst. Prof., Aalto University, Department of Information and Service Management, Finland; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandIn difficult optimization problems, strong formulations and algorithmic techniques that exploit the problem structure are often invaluable in designing efficient solution methods. Although microprocessors and generic solvers have reduced solution times, these tools are often not enough to solve hard problems of realistic size. To overcome these challenges, the standard practice has typically been to develop tailored, problem-specific algorithms. This summary chapter introduces efficient formulations and algorithms for three different optimization problems, each of which either serves as a basis for future extensions or unifies previous approaches under one framework. First, two algorithms are developed for the green vehicle routing problem. Both algorithms rely on a novel multigraph reformulation that transforms refueling nodes into non-dominated refuel paths between customers. This transformation allows combining routing and refueling decisions with negligible overhead. Both algorithms serve as building blocks for developing new solution methods for generalizations of the problem. The effectiveness of the multigraph and the developed algorithms are demonstrated through computational evaluation. Second, a new framework for centralized allocation of resources to a portfolio of decision-making units is developed. This framework can handle multiple objectives with incomplete preferences and compute all non-dominated portfolios satisfying these preferences. Each portfolio corresponds to a Pareto-optimal allocation of resources among the decision-making units that maximizes portfolio-level efficiency. The framework unifies several previous models that compute single solutions from the efficient frontier, possibly involving non-linear utilities and many kinds of production possibility sets. It also demonstrates that relying on conventional efficiency scores in guiding resource allocation decisions may lead to inefficiencies at the portfolio level. Third, a novel Decision Programming approach is developed that contributes towards unifying stochastic programming and decision analysis within a single framework and relaxes two common assumptions in decision analysis: (i) perfect recall where all prior decisions are known when making a decision and (ii) regularity that assumes a total temporal order for decision variables. Decision Programming relies on a mixed-integer linear programming formulation that can handle both endogenous and exogenous uncertainties and can also optimize problems involving simultaneous decisions by agents unable to communicate with each other. The Decision Programming framework can be extended to incorporate deterministic and chance constraints, and it can be harnessed to compute all non-dominated solutions in presence of multiple value functions. Most importantly, it contributes towards approaches that can solve problems from both decision analysis and stochastic programming, and may thus facilitate collaboration between these two sub-disciplines in the future.Item Optimization Models for Assessing Energy Systems in Transition(Aalto University, 2019) Virasjoki, Vilma; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Siddiqui, Afzal, Prof., University College London, UK, Stockholm University, Sweden, Aalto University, Finland, HEC Montréal, Canada; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandEnergy systems are undergoing a major transition toward environmental sustainability. For instance, the European Union has implemented energy and climate policy targets for years 2020 and 2030 in order to reduce greenhouse gas emissions, increase renewable energy production and improve energy efficiency. However, because variable renewable energy sources (VRES) such as wind and solar power are intermittent, more flexibility is required from the energy system. This dissertation analyzes the present energy transition through two lenses. First, it formulates mathematical models which are solved through optimization and complementarity techniques to determine optimal investment and operational decisions, in recognition of the stakeholders' different and even conflicting objectives. Second, these models provide insights into the Western European power market and Nordic energy systems by helping in the assessment of the technical, welfare, and emissions impacts of large-scale energy storage. Much emphasis is placed on the analysis of market efficiency, because large producers, in particular, may be able to affect markets in their favor. This kind of market power is studied especially in connection with investments into and operation of energy storage as well as the production of combined heat and power (CHP). Finally, the modeling of power transmission networks gives information about the combined effects of interconnected markets and the increasing share of VRES. The models in this dissertation support energy policy-making in the present situation in which it is crucial to understand how the security of supply can be maintained without compromising sustainability and market efficiency. Overall, the models yield results which could hardly be obtained through empirical research, as the outcomes of ongoing developments and planned policies depend on the actions of all stakeholders.Item Portfolio decision analysis for infrastructure and innovation management(Aalto University, 2017) Mild, Pekka; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Liesiö, Juuso, Prof., Aalto University, Department of Information and Service Economy, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandPractically all companies and public agencies make decisions about resource allocation among investment categories and selection of 'lumpy' investments such as projects. While economic calculations tend to be central in these decisions, it is often necessary to account for hard-to-monetize impacts, multiple objectives, stakeholder interests and relevant constraints. For these decisions, Portfolio Decision Analysis (PDA) provides mathematical methods and decision support processes that assist in the construction of portfolios consisting of a set of discrete alternatives. This Dissertation develops a multi-criteria PDA methodology, Robust Portfolio Modeling (RPM), for problems in which a subset of indivisible projects is to be selected from a large number of candidates, with the aim of contributing to the attainment of multiple objectives while satisfying relevant budgetary and other constraints. The RPM methodology – which is based on the linear-additive value representation – admits incomplete information about criterion weights, project scores and project costs in portfolio selection. A key concept of the RPM, the core index, derives project-level recommendations by computing and analyzing non-dominated project portfolios. These recommendations show (i) which projects are robust choices in the light of the incomplete information and (ii) which projects are promising targets for acquiring additional information that can lead to more conclusive results concerning their inclusion or exclusion to the final portfolio. The Dissertation also presents real-life applications of PDA in infrastructure and innovation management. The RPM application on bridge maintenance management (Paper [III]) found its way into repeated use at the Finnish Transport Agency. The second infrastructure application (Paper [IV]) is an innovative combination of standard Operations Research methods to support strategic resource allocation between road asset categories and types of operations. The third application (Paper [V]) demonstrates how the RPM can be utilized to ex post evaluation to identify sets of over- and underperforming projects in an innovation program. The Dissertation shows that the RPM method and its key concepts are readily understood and accepted by practitioners, including senior managers. The method can be tailored to utilize existing project data from sources such as monitoring databases, and it is computationally capable of processing hundreds of project candidates. Moreover, given that repeated real-life applications are relatively rare in decision analysis literature, the methodological development in this Dissertation already can be viewed as a pioneering platform for further avenues of PDA research.Item The principle of least action and stochastic dynamic optimal control — Applications to economic, financial and physical systems(Aalto University, 2021) Lindgren, Jussi; Liukkonen, Jukka, Dr., Radiation and Nuclear Safety Authority, Finland; Salo, Ahti, Prof., Aalto University, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandEconomic and financial systems as well as the physical laws of nature can be studied within a common mathematical framework. In particular, the principle of least action and stochastic optimal control can be applied both to resource allocation problems within the society, as well as to derive physical laws. In economic and financial systems, optimal performance is vital, given that economic policies affect all citizens and general welfare. It is also paramount to try to understand the mathematical structure of efficient financial markets. Both these issues are discussed in this Dissertation. First, a stochastic optimal control model is developed to model the dynamics of public debt. In such a dynamical model of public debt, the variance of the debt to GDP ratio is determined in order to assess the risk of insolvency. The model demonstrates also the risks stemming from various feedback mechanisms due to hidden fiscal multipliers and hidden credit risk premia. The model is potentially useful for finance ministries and national debt managers and investors alike. Second, stochastic optimal control is used to derive the key pricing equation from finance theory as an optimality condition for the financial market to be informationally efficient. With such assumptions a nonlinear transport equation is derived for the market instantaneous returns. The model could be used to predict average returns on various assets. Thus the model could be useful for asset managers and investment professionals. Third, it is shown how the key equations of quantum mechanics can also be derived as an optimality condition, when there is background noise stemming from the spacetime fluctuations at small scales. Furthermore, the Heisenberg uncertainty principle is derived from the stochastic optimal control model. Finally, the field equations of electromagnetism are derived from a least action principle and it is shown how Maxwell's equations relate to the Einstein field equation. In particular, the link of electromagnetism and spacetime curvature could be tested empirically in principle and the results could facilitate further engineering applications. The results indicate that strive for efficiency is abundant in natural as well as in economic and financial systems and that the principle of least action is even more omnipresent and important than previously has been known.Item Resource allocation methods for defence and infrastructure systems(Aalto University, 2016) Kangaspunta, Jussi; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandOrganizations initiate research and development projects as well as other investments in order to build and maintain long-term capabilities that contribute the attainment of their strategic objectives. Typically they have limited resources and can therefore select only a subset of available investment alternatives when seeking to maximize the overall value which is defined in terms of multiple objectives. These decisions can be challenging due to uncertainties about the future value of investment alternatives, interdependencies between alternatives, and possible conflicting preference statements about the relative importance of the objectives. This Dissertation develops methods to support the allocation of resources to investment alternatives by evaluating portfolios (i.e. combinations) of alternatives. These methods help identify portfolios which are efficient with regard to multiple levels of resource expenditures so that statements from multiple information sources (e.g. expert judgments and simulation models) are transparently taken into account. Furthermore, the methods permit analyses with regard to different scenarios that portray possible states of future and handle incomplete information about the importance of objectives. The proposed resource allocation methods are illustrated by examining problems from defence planning and critical infrastructure risk management. Both of these interconnected application areas are under budgetary pressure while national security threats are taking increasingly diverse forms. The examples show that the developed methods and computational algorithms can be usefully applied. Moreover, the methods are also generic enough to be used in other areas as well. For instance, the energy sector needs to provide sufficient supply while ensuring that the energy production system is resilient and able to recover from shocks.Item Robust reliability and resource allocation - Models and algorithms(Aalto University, 2016) Toppila, Antti; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandOrganizational decision makers (DMs) such as companies, institutions and public sector agencies rely on mathematical models for decision support. Often these models have parameters such as probabilities of events and outcomes of actions, which typically are epistemically uncertain due to the lack of historical data or other information. In such cases, DMs often need to understand how this epistemic uncertainty impacts the decision recommendations. This Dissertation considers models for supporting allocation decisions in settings where epistemic uncertainty is modeled explicitly through incomplete information. The resulting decision recommendations that account for epistemic uncertainty are derived through dominance: Alternative A dominates alternative B if A is at least as good as B for all parameters that are compatible with the available incomplete information, and moreover, strictly better for some. A dominated alternative should not be selected, because there exist at least one alternative that is not worse for any parameters and is strictly better for some. Thus, the decision recommendation to select an alternative that is non-dominated (ND) is robust with respect to the epistemic uncertainty. In the models considered in this Dissertation, generating the ND alternatives leads to a computationally challenging combinatorial optimization problem. Several exact algorithms and approximative methods for computing the ND alternatives are developed. The exact methods are based on classical dynamic programming and branch-and-bound algorithms, as well as binary decision diagrams, which have recently been used in solving challenging optimization problems. The simplification methods, on the other hand, are more ad hoc in nature and based on problem specific approaches. This Dissertation contributes by providing ways for analyzing the impact of epistemic uncertainty with incomplete information in application areas which are central in the fields of risk analysis and decision analysis, namely (i) probabilistic risk analysis based on importance measures, (ii) allocation of resources to reliability enhancing actions, (iii) project portfolio selection, and (iv) resource allocation to standardization activities. The developed methods are generic in that they could likely be adopted with small refinements even in other application areas.Item Theoretical and methodological extensions to dynamic reliability analysis(Aalto University, 2017) Tyrväinen, Tero; Holmberg, Jan-Erik, Dr., Risk Pilot AB, Finland; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, Finland; Matematiikan ja systeemianalyysin laitos; Department of Mathematics and Systems Analysis; Systems Analysis Laboratory; Perustieteiden korkeakoulu; School of Science; Salo, Ahti, Prof., Aalto University, Department of Mathematics and Systems Analysis, FinlandRigorous analysis of the reliability of a dynamic system calls for modelling of the dynamic behaviour of the system and its interactions. However, traditional and the most frequently used reliability analysis methods, such as fault tree analysis, are static and have only limited capability to represent dynamic systems. Therefore, dynamic reliability analysis methods have been studied since 1990s. Dynamic flowgraph methodology (DFM) is a method for the reliability analysis of dynamic systems containing feedback loops. A DFM model is a dynamic graph representation of the analysed system. DFM has been most often applied to different digital control systems. One reason for this is that a DFM model can represent the interactions between a control system and the controlled process. The main goal of DFM analysis is to identify prime implicants, which are minimal combinations of events and conditions that cause the analysed top event, for example, system failure. This dissertation strengthens the mathematical foundation of DFM by developing an improved definition of a prime implicant. Risk importance measures can be used to identify components and basic events that are most important for the reliability of the system. This dissertation develops new dynamic risk importance measures as generalisations of two traditional risk importance measures for the needs of DFM. Unlike any other importance measure, the dynamic risk importance measures utilise all the information available in prime implicants of DFM. They primarily measure the importances of different states of components and variables of a DFM model. The computation of the dynamic risk importance measures for failure states of components provides significant additional information compared to other importance values. This dissertation also examines common cause failures (CCFs) in dynamic reliability analysis. Taking CCFs into account is important when modelling systems with redundancies. The dissertation extends the DFM by presenting CCF models that take failure times of components into account.