Decision Support Tools (DSTs) are commonly utilised within the Asset Management (AM) operations of infrastructure organisations. These manual or computerised tools are used to support decisions about what assets to acquire and how to operate them. Their performance can therefore have significant financial and non-financial implications for a business. Despite their importance, managing the performance of DSTs after implementation has received only limited attention within the literature. The output of this research is a conceptual approach for managing the performance of decision support tools used within an Asset Management context. It encompasses a risk-based DST Performance Management Process and DST Performance Assessment Techniques (the methods for applying the process in an industry setting).The novelty of the approach: (1) Alignment with the fundamental principles of the International Standard for Asset Management, ISO 5500x:2014. Thus, consistency of the management of DSTs with other assets types. (2) A generic process that is tailored to the context of the specific organisation. (3) Consistency with the risk management process (ISO 31000:2009) and meeting the requirements for a quality process defined within the Quality Management Standard (ISO 9000: 2015). (4) A cyclical process design ensuring that the approach, and how the approach is applied within an industry setting, will evolve to reflect the changing environment. A case study and the input of subject matter experts from within National Grid Electricity Transmission was used to both inform and evaluate the conceptual approach design. A semi-structured interview, with a water sector subject matter expert, assesses the transferability of the approach to a wider Asset Management population. The results of the evaluation demonstrate the conceptual approach to be both logical and useable in each context. The future research pathway looks to progress the conceptual approach through to industry adoption.

In order to deal with the mounting issue of Municipal Solid Waste (MSW) in a way that is in line with sustainable development and Integrated Solid Waste Management (ISWM) a systems approach is necessary.This approach can practically be integrated into the MSW decision-making process through Life Cycle Thinking(LCT) and environmental systems analysis tools such as Life Cycle Assessment (LCA). This paper is written within the context of the RECO Baltic 21 Tech (RB21T) project which aims to improve waste management practices in 12 countries in the Baltic Sea Region. The main aim of this paper is to investigate the extent to which LCA is used as decision-support in MSW management in Sweden, Estonia and Lithuania. The use of LCA is examined on a national level as well as on a local or regional level based on relevant literature and a set of interviews conducted in each country. According to the results the use of LCA as decision-support in MSW management is very limited in Estonia and Lithuania whilst it is already a well-established tool in Sweden. Most of the LCA efforts in the Baltic States have been conducted in connection with foreign projects and investments,such as RB21T. Although an actual LCA might not always be applied in Sweden, LCT is prevalent in MSW management both on a national and local level. In order for LCA to be better integrated into MSW management this paper argues that there is a need for increased knowledge, data, more user-friendly LCA-tools andstrengthening regional partnerships for further transfer of knowledge between countries.

This dissertation explores reifying the management science concept of organizations as a collection of decisions. Organizational management entails resource allocation activities that can be formulated in terms of elementary relational functions. All elasticity-type formulations, most generic "production" functions, and various projection models that organizations might require (such as sales forecasts) can all be represented by elementary relational functions. Therefore, information systems in organizations can be representative of relationships between decision requirements, as theorized in relational model bases. A relational model-base structure acts as an integrative device by relating an organization's elementary relational functions to each other, with all that is kept for any model being the current values for coefficients and the now prevailing parametric values for the state variables of the model.Anchoring management information systems around relational model bases is particularly appropriate for organizations that have some reliance on real-time management decision making by providing the answer to two requirements for such organizations: one being the requirement for more accurate and current real-time, operational decision making within the organization; the other being the integration of functions for decision-making purposes within an organization. Relational model bases thus enable more dynamic management and become a central information system type for organizations that have dynamic resource allocation requirements that can employ technical tactics around such relational model bases. The relational model base would reflect revealed needs in an organization as opposed to projected needs, easing an organization's reliance on forecasting and moving it toward real-time decision making. The case for the introduction of these information systems is further strengthened by the fact that relational model base-type structures are already operating in production environments within organizations. The methodology used in this dissertation involved modeling organizational decision requirements in particular organizational cases to determine the behavior of relational model bases within those prototypical organizations and the application of relational model bases to real-time decision making. The first organizational scenario is a recursive agribusiness e-commerce case, with the target application being precision agriculture. The second scenario is a non-recursive ecological economics case, with the target application being preservation of biodiversity through land (habitat) protection.

Optimization of time and cost in construction projects has been subject to extensive research since the development of the Critical Path Method (CPM). Many researchers have investigated various versions of the well-known Time-Cost Trade-off (TCT) problem including linear, convex, concave, and also the discrete (DTCT) version. Traditional methods in the literature for optimizing time and cost of construction projects range from mathematical methods to evolutionary-based ones, such as genetic algorithms, particle swarm, ant-colony, and leap frog optimization. However, none of the existing research studies has dealt with the optimization of large-scale projects in which any small saving would be significant. Traditional approaches have all been applied to projects of less than 100 activities which are far less than what exists in real-world construction projects. The objective of this study is to utilize recent developments in computation technology and novel optimization techniques such as Constraint Programming (CP) to improve the current limitations in solving large-scale DTCT problems. Throughout the first part of this research, an Excel-based TCT model has been developed to investigate the performance of traditional optimization methods, such as mathematical programming and genetic algorithms, for solving large TCT problems. The result of several experimentations confirms the inefficiency of traditional methods for optimizing large TCT problems. Subsequently, a TCT model has been developed using Optimization Programming Language (OPL) to implement the Constraint Programming (CP) technique. CP Optimizer of IBM ILOG Optimization Studio has been used to solve the model and to successfully optimize several projects ranging from a small project of 18 activities to very large projects consisting of more than 10,000 activities. Constraint programming proved to be very efficient in solving large-scale TCT problems, generating substantially better results in terms of solution quality and processing speed. While traditional optimization methods have been used to optimize projects consisting of less than one hundred activities, constraint programming demonstrated its capability of solving TCT problems comprising of thousands of activities. As such, the developed model represents a significant improvement in optimization of time and cost of large-scale construction projects and can greatly enhance the level of planning and control in such projects.

Optimization of time and cost in construction projects has been subject to extensive research since the development of the Critical Path Method (CPM). Many researchers have investigated various versions of the well-known Time-Cost Trade-off (TCT) problem including linear, convex, concave, and also the discrete (DTCT) version. Traditional methods in the literature for optimizing time and cost of construction projects range from mathematical methods to evolutionary-based ones, such as genetic algorithms, particle swarm, ant-colony, and leap frog optimization. However, none of the existing research studies has dealt with the optimization of large-scale projects in which any small saving would be significant. Traditional approaches have all been applied to projects of less than 100 activities which are far less than what exists in real-world construction projects. The objective of this study is to utilize recent developments in computation technology and novel optimization techniques such as Constraint Programming (CP) to improve the current limitations in solving large-scale DTCT problems. Throughout the first part of this research, an Excel-based TCT model has been developed to investigate the performance of traditional optimization methods, such as mathematical programming and genetic algorithms, for solving large TCT problems. The result of several experimentations confirms the inefficiency of traditional methods for optimizing large TCT problems. Subsequently, a TCT model has been developed using Optimization Programming Language (OPL) to implement the Constraint Programming (CP) technique. CP Optimizer of IBM ILOG Optimization Studio has been used to solve the model and to successfully optimize several projects ranging from a small project of 18 activities to very large projects consisting of more than 10,000 activities. Constraint programming proved to be very efficient in solving large-scale TCT problems, generating substantially better results in terms of solution quality and processing speed. While traditional optimization methods have been used to optimize projects consisting of less than one hundred activities, constraint programming demonstrated its capability of solving TCT problems comprising of thousands of activities. As such, the developed model represents a significant improvement in optimization of time and cost of large-scale construction projects and can greatly enhance the level of planning and control in such projects.

In the expansion of offshore sustainable energy systems, there is growing pressure on the environment and permit processes and the accumulation results in much higher total risk for accidents of future assets. Anticipating the problems at the design stage and improving verification is likely to increase energy development and reduce costs. This thesis explores offshore DST (Decision Support Tools) and risk verification of subsea cable assets.For subsea cables, a statistical method is proposed utilizing measurement data together with shipping traffic data (AIS) to estimate the environmental risk and risk of accidents of installed cable assets. This should partially solve issues of improving design using more data and surveys and utilizing mechanical and sensor-specific characteristics to improve the confidence and burial estimation, contrary to today’s methodology. The implication of the two studies of cable burial risk assessment techniques and verification shows how a developed methodology can solve issues for verifying the integrity of an installed asset. Putting our methodology into practice involves many challenges.  For the marine Decision Support Tool (DST) and sustainable energy development, to estimate potential savings if permit processes would be shorter and less burdensome without degrading the quality of the EIA (Environmental Impact Assessment). A method is proposed to model various scenarios of effective savings from the development of a DST to reduce costs spent on EIA permitting by the offshore energy developers. The study of the implication of the marine EIA DST shows a quantifiable estimate of the savings potential for permit processes for sustainable offshore development, and results indicate a need for optimization of DST development, which can be an essential factor in its implementation and success.

<p>The research effort addressed important gaps in the modeling to simulate Urban Air Mobility (UAM) operations and couple optimization analyses for vehicle design, fleet allocations, and operational choices for next generation urban travel. Urban Air Mobility is expected to be a \$1 trillion dollar industry by 2040, but operators and designers have limited models and tools to estimate fleet performance, cost metrics, emissions performance, and profit for a given concept under future concepts of operations. A review of the literature reveals 14 modeling gaps related to infrastructure, operations, airspace, vehicles, and customers. In addition, the UAM industry requires better understanding of how operational choices may impact vehicle design and fleet allocations in a market with significant economic barriers and infrastructure needs. To address those needs, this effort proposed alternatives to address modeling challenges and develop studies to evaluate UAM vehicle concepts and concepts of operations in ways once not possible using the enhanced modeling tools. The research findings revealed that modeling coupled design/fleet and operational choices can affect daily profitability potential by 2-4\times\, for piloted and autonomous operations and affect the fleet size from between 12-50 vehicles across small, medium, and large metropolitan areas. The modeling capability provided by the improvements in UAM operations simulations and accessing vehicle and fleet metrics enables future studies to address UAM in a holistic manner. The increased capability could benefit the UAM community and inform future operations and concepts of operations in preparation for ubiquitous operations.</p>

The numerous buildings that currently require thermal rehabilitation in Romania means that substantial resources and a large number of competent people are required to carry out surveys and energy audits. However, commercial energy balance software is mostly unaffordable for those organisations involved in this process. This paper describes an energy balance programme – ENEFControl – developed to be a rapid, low cost, local tool able to assist in the choice of energy efficient solutions for buildings. To test the software, thermal and energy analyses were carried out on a 1970s built apartment block in Transylvania. Based on these analyses, three constructive scenarios were proposed for thermal rehabilitation. Compared to the performance of the analysed building, the thermal and energy performance of the retrofitted building in all three scenarios significantly improved. Since European Union accession in 2007, rapidly rising energy costs have affected the Romanian population. ENEFControl offers Romanian engineers and architects an opportunity to speed up the rehabilitation programme of buildings without the need for more expensive expertise and tools.

Climate change-related hazards negatively impact ecosystems, economies, and quality of life. Significant resources have been invested in data collection and research with the goal of enhanced understanding and capacity to predict future conditions in order to mitigate or adapt to intensifying hazard risk. The expansive production of climate science has generated a necessary complimentary enterprise dedicated to enhancing decision-makers’ understanding of and access to climate science as it is essential for future societal and ecological well-being. Though the aim of these many tools is to support resilient decision-making in the face of climate change, professionals report an underutilization of climate resilience tools. It has been suggested that stakeholder engagement during climate resilience tool development will improve the rates of use; however, there have been no studies to explore if the findings from tool diffusion and adoption studies in other sectors translate to climate resilience tools. An end-user engagement process for the development of a climate resilience tool was established and implemented. The process itself and the outcomes of the process, in this case an online climate decision-support tool called Gulf TREE (www.GulfTREE.org), were studied. Findings included documenting that end-user engagement during climate resilience tool development, while more costly and time intensive, does lead to increased rates of diffusion and adoption of a climate resilience tool through both direct and indirect means. This work demonstrated that pre-development engagement to scope tool development is critical for maximizing relative benefit of a climate resilience tool. Additionally, all phases of engagement are necessary for both a useable and useful tool because each phase contributes to different attributes of the tool. Further research areas identified include understanding how much and what kind of stakeholder engagement is necessary to support continued diffusion and adoption after a tool is released, the role that mandates in climate resilience has on the adoption and diffusion of climate resilience tools, and how to define if a climate resilience tool has been successful.

Doctor of Philosophy / Department of Environmental Design and Planning Program / Lee R. Skabelund / Despite the growing number of urban adaptation planning initiatives to climate change hazards, there exist significant barriers related to implementation uncertainties that hinder translation of adaptation plans into actions, resulting in a widely recognized ‘planning-implementation gap’ across scales and regions. Bridging the planning-implementation gap will require overcoming implementation uncertainties by better understanding the relationships between the primary factors driving adaptation planning initiatives and emerging adaptation options across spatial scales. The modified Driver-Pressure-State-Impact-Response model published by Rounsevell, Dawson, and Harrison in 2010 provided a robust framework for identifying the primary factors driving adaptation planning initiatives and the emerging adaptation options related to risk of changing climate and flooding events in the urban context. Drawing on evidence from the systematic review of 121 adaptation planning case studies across North America, this research derived qualitative and quantitative data, which was subsequently analyzed using binary logistic regression to generate objective and generalizable findings. The findings of binary logistic regression models suggest that the choice of specific adaptation options (namely enhancing adaptive capacity; management and conservation; and improving urban infrastructure, planning, and development) may be predicted based on the assessment of primary factors driving adaptation planning initiatives (namely, anticipation of economic benefits; perceived threats to management and conservation of urban natural resources; support of human and social systems; and improvement of policy and regulations) in relation to the risk of changing climate and urban flooding events. This does not imply that other primary factors (namely information and knowledge; perceived funding and economic opportunities; evidence of climate change effects; and general concerns) have no or insignificant relationships with the selection of adaptation options, only that the review did not find evidence to support such claims. These study findings may offer useful guidance to the design and further development of planning and decision support tools that could be used for assessment of adaptation plans and selection of robust adaptation options that take account of uncertainties surrounding implementation of effective climate adaptation actions. Study findings can also inform evidence-based policy and investment decision making, especially in regions where urban adaptation plans are weak or absent.