Municipalities increasingly have less financial resources to spend on implementation of transport strategies and plans. This situation is putting pressure on transport professionals to minimize wasteful expenditure on projects that do not deliver high transport service improvements. As such, the need for efficient, pragmatic decision making on policy direction, infrastructure expenditure, or any transport interventions is becoming very critical. Thus, transport professionals are increasingly in need of tools to help them predict with increased accuracy the outcomes of their intended transport interventions. The City of Cape Town has a Bus Rapid Transport system called MyCiTi. Current MyCiTi operations are incurring losses. The service is kept running on the back of subsidies from the federal government. There is a need for rationalization of the system. However, with strained resources, the interventions on the system have to guarantee improvements. Overemphasis on the ability of MyCiTi BRT service to support transportation during the 2010 soccer world cup event heavily influenced the design of the network. As a result, network appraisal is one area that can be done on the system to identify areas of improvement. In this thesis, decision making support will be demonstrated using a network design appraisal process for the MyCiTi BRT system in Cape Town. The existing MyCiTi network will undergo network improvement using heuristic node insertion technique leading to multiple network scenarios in a modeling environment. Agent-Based demand mobility behavior simulation will be used on each of the network scenarios to come up with network performance indicators. These network performance indicators will be used in the multi-criteria decision analysis (MCDA) model to come up with a ranking of the network scenarios and help in deciding on the optimum network improvement intervention. Overall, findings of this research show the importance of weighting of the performance indicators. Where networks that score well in the performance indicator with the high weights also rank high. In conclusion, the study has demonstrated the importance of decision making support in interventions on complex systems like bus systems. Recommendations on the possible avenues of research stemming from this thesis have also been outlined.

L’aide multicritère à la décision (AMCD) vise à faciliter et améliorer la qualité du processus de prise de décision. Les méthodes d’AMCD permettent de traiter les problèmes de choix, rangement et classification. Ces méthodes impliquent généralement la construction d’un modèle. Déterminer les valeurs des paramètres de ces modèles n’est pas aisé. Les méthodes d’apprentissage indirectes permettent de simplifier cette tâche en apprenant les paramètres du modèle de décision à partir de jugements émis par un décideur tels que “l’alternative a est préférée à l’alternative b” ou “l’alternative a doit être classifiée dans la meilleure catégorie”. Les informations données par le décideur sont généralement parcimonieuses. Le modèle d’AMCD est appris au cours d’un processus interactif entre le décideur et l’analyste. L’analyste aide le décideur à formuler et revoir ses jugements si nécessaire. Le processus s’arrête une fois qu’un modèle satisfaisant les préférences du décideur a été trouvé. Le “preference learning” (PL) est un sous domaine du “machine learning” qui s’intéresse à l’apprentissage des préférences. Les algorithmes de ce domaine sont capables de traiter de grands jeux de données et sont validés au moyen de jeux de données artificiels et réels. Les jeux de données traités en PL sont généralement collectés de différentes sources et sont entachés de bruit.Contrairement à l’AMCD, il existe peu ou pas d’interaction avec l’utilisateur en PL. Le jeu de données fourni en entrée à l’algorithme est considéré comme un échantillon éventuellement bruité d’une “réalité” ou “vérité de terrain”. Les algorithmes utilisés dans ce domaine ont des propriétés statistiques fortes leur permettant de s’affranchir du bruit dans ces jeux de données. Dans cette thèse, nous développons des algorithmes d’apprentissage permettant d’apprendre lesparamètres de modèles d’AMCD. Plus précisément, nous développons une métaheuristique afin d’apprendre les paramètres d’un modèle appelé MR-Sort (“majority rule sorting”). Cette métaheuristique est testée sur des jeux de donnéesartificiels et réels utilisés dans le domaine du PL. Nous utilisons cet algorithme afin de traiter un problème concret dans le domaine médical. Ensuite nous modifions la métaheuristique afin d’apprendre les paramètres d’un modèle plus expressif appelé NCS (“non-compensatory sorting”). Finalement, nous développons un nouveau type de règle de veto pour les modèles MR-Sort et NCS qui permet de prendre les coalitions de critères en compte. La dernière partie de la thèse introduit les méthodes d’optimisation semi-définie positive (SDP) dans le contexte de l’aide multicritère à la décision. Précisément, nous utilisons l’optimisation SDP afin d’apprendre les paramètres d’un modèle de fonction de valeur additive. / Multiple-criteria decision analysis (MCDA) aims at providing support in order to make a decision. MCDA methods allow to handle choice, ranking and sorting problems. These methods usually involve the elicitation of models. Eliciting the parameters of these models is not trivial. Indirect elicitation methods simplify this task by learning the parameters of the decision model from preference statements issued by the decision maker (DM) such as “alternative a is preferred to alternative b” or “alternative a should be classified in the best category”. The information provided by the decision maker are usually parsimonious. The MCDA model is learned through an interactive process between the DM and the decision analyst. The analyst helps the DM to modify and revise his/her statements if needed. The process ends once a model satisfying the preferences of the DM is found. Preference learning (PL) is a subfield of machine learning which focuses on the elicitation of preferences. Algorithms in this subfield are able to deal with large data sets and are validated withartificial and real data sets. Data sets used in PL are usually collected from different sources and aresubject to noise. Unlike in MCDA, there is little or no interaction with the user in PL. The input data set is considered as a noisy sample of a “ground truth”. Algorithms used in this field have strong statistical properties that allow them to filter noise in the data sets.In this thesis, we develop learning algorithms to infer the parameters of MCDA models. Precisely, we develop a metaheuristic designed for learning the parameters of a MCDA sorting model called majority rule sorting (MR-Sort) model. This metaheuristic is assessed with artificial and real data sets issued from the PL field. We use the algorithm to deal with a real application in the medical domain. Then we modify the metaheuristic to learn the parameters of a more expressive model called the non-compensatory sorting (NCS) model. After that, we develop a new type of veto rule for MR-Sort and NCS models which allows to take criteria coalitions into account. The last part of the thesis introduces semidefinite programming (SDP) in the context of multiple-criteria decision analysis. We use SDP to learn the parameters of an additive value function model.

The problem of project selection is of significant importance in management of information systems. Almost $2 trillion is spent worldwide every year on IT projects, with over $600 billion spent in the US alone. Traditionally, managers have being using the classical net present value (NPV) method in conjunction with multicriteria scoring models for ROI analysis and selection of IT project investments The multicriteria models use ad-hoc evaluation criteria to assign priority weights and then rate the alternatives against each criterion. These models have two limitations. First, the criteria and weights are based on subjective judgments, allowing the introduction of politics in the information management decision process and the generation of arbitrary results. Second, the classical approach uses deterministic estimations of the cost, benefits and the returns of the projects, without considering the impact of uncertainty and risk in the business decisions. This research proposed a better alternative for ROI analysis and selection of IT projects using a real option strategic scorecard (ROSS) approach. In contrast with traditional methodologies and previous research work, the ROSS decision framework uses a more comprehensive, axiomatic approach for systematically measuring both the business value and the strategic implications of IT project investments. The ROSS approach integrates in a unified IT project management decision framework the best elements of real option theory, strategic balanced scorecards, Monte Carlo simulations and analytical network processes to fully analyzes the effect of uncertainty and risk in the IT investment decisions. In addition, the ROSS approach complies with the critical success factors that have being identified in the literature for validation of IT decision frameworks. The main benefit of the ROSS approach is to enable managers to better compare and rank projects in the IT portfolio, optimizing the ROI analysis and selection of information system projects.

The thesis is divided into two parts concerning different topics. The first is solving a multi-period portfolio decision problem, and the second, more theoretical, is a numerical comparison of uncertainty measures within evidence theory. Nowadays, portfolio problems are very common and present in several fields of study. The problem is inspired by a real-world infrastructure manage- ment case in the energy distribution sector. The problem consists of the optimal selection of a set of activities and their scheduling over time. In scheduling, various constraints and limits that the company has to meet must be considered, and the selection must be based on prioritizing the activities with a higher priority value. The problem is addressed by Port- folio Decision Analysis: the priority value of activities is assigned using the Multi-Attribute Value Theory method, which is then integrated with a multi-period optimization problem with activities durations and con- straints. Compared to other problems in the literature, in this case, the ac- tivities have different durations that must be taken into account for proper planning. The planning obtained is suitable for the user’s requirements both in terms of speed in providing results and in terms of simplicity and comprehensibility. In recent years, measures of uncertainty or entropy within evidence theory have again become a topic of interest in the literature. However, this has led to an increase in the already numerous measures of total uncertainty, that is, one that considers both conflict and nonspecificity measures. The research aims to find a unique measure, but none of those proposed so far can meet the required properties. The measures are often complex, and especially in the field of application, it is difficult to understand which is the best one to choose and to understand the numerical results obtained. Therefore, a numerical approach that compares a wide range of measures in pairs is proposed alongside comparisons based on mathematical proper- ties. Rank correlation, hierarchical clustering, and eigenvector centrality are used for comparison. The results obtained are discussed and com- mented on to gain a broader understanding of the behavior of the measures and the similarities and non-similarities between them.

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Sustainable pavement management implies maintaining acceptable condition of pavements while also considering the tradeoff between cost, environmental impacts and social impacts of pavement investments. Typical pavement management practices only consider economic considerations, and environmental mitigation techniques are employed after the selection of the maintenance action is complete. This dissertation presents a series of papers that demonstrate the impact of decision making on the environmental impact of the pavements both at the project and network levels of pavement management. An analysis was conducted of two models that relate pavement properties to vehicle rolling resistance and fuel consumption. These models were used, along with other tools to evaluate the impact of including the use phase of a pavement into pavement lifecycle assessments. A detailed project level lifecycle assessment was conducted, and it was found that the vehicles on the pavement during the use phase contribute the most to environmental pollutants by a significant margin over other phases of the lifecycle. Thus, relatively small improvements in the factors which contribute to rolling resistance may significantly influence the environmental impacts of the pavement. Building on this, a network level lifecycle assessment method was proposed to probabilistically quantify energy consumption for a given set of expected maintenance actions. It was shown that, although maintenance actions require a certain amount of energy consumption, this energy can be offset by improved road conditions leading to reduced rolling resistance. However, this tradeoff of reduced energy consumption also includes increased costs for a given network condition. In other words, the lowest energy consumption values did not tend to fall along the line defined by minimizing the cost divided by the pavement condition. In order to demonstrate how this tradeoff should be addressed, a novel decision analysis framework was developed, and implemented on a specific pavement network. Finally, a survey of transportation professionals was evaluated to determine their optimal points within the solution space defined by minimizing costs and energy consumption while maximizing pavement condition. It was found that the solution space could be greatly reduced by implementing their responses using the proposed decision analysis framework. / Ph. D.

Yes / Social media usage in evacuations and emergency management represents a rapidly expanding field of study. Our paper thus provides quantitative insight into a serious practical problem. Within this context a behavioural approach is key. We discuss when facilitators should consider model-based interventions amid further implications for disaster communication and emergency management. We model the behaviour of individual people by deriving optimal contrarian strategies. We formulate a Bayesian algorithm which enables the optimal evacuation to be conducted sequentially under worsening conditions. / Supported by EPSRC (IDEAS Factory - Game theory and adaptive networks for smart evacuations, EP/I005765/1)

Currently several disparate and incomplete approaches are being used to analyse and make decisions on the complex methodology of carbon capture and storage (CCS). A literature review revealed that, as CCS is a new and complex technology, there is no agreed-upon thorough assessment method for high-level CCS decisions. Therefore, a risk model addressing these weaknesses was created for assessing complex CCS decisions using a multi-criteria decision analysis approach (MCDA). The model is aimed at transparently and comprehensively assessing a wide variety of heterogeneous CCS criteria to provide insights into and to aid decision makers in making CCS-specific decisions. The risk model includes a variety of tools to assess heterogeneous CCS criteria from the environmental, social, economic and engineering fields. The model uses decision trees, sensitivity analysis and Monte Carlo simulation in combination with utility curves and decision makers’ weights to assess decisions based on data and situational uncertainties. Elements in the model have been used elsewhere but are combined here in a novel way to address CCS decisions. Three case studies were developed to run the model in scenarios using expert opinion, project-specific data, literature reviews, and engineering reports from Alberta, Saskatchewan and Europe. In collaboration with Alberta Innovates Technology Futures, a pilot study was conducted with CCS experts in Alberta to assess how they would rank the importance of CCS criteria to a project selection decision. The MCDA model was run using experts’ criteria weights to determine how CCS projects were ranked by different experts. The model was well received by the CCS experts who believed that it could be adapted and commercialized to meet many CCS decision problems. The survey revealed a wide range in experts’ understanding of CCS criteria. Experts also placed more emphasis on criteria from within their field of expertise, although economic criteria dominated weights overall. The results highlight the benefit of a model that clearly demonstrates the trade-offs between projects under uncertain conditions. The survey results also revealed how simple decision analyses can be improved by including more transparent methods, interdisciplinary criteria and sensitivity analysis to produce more comprehensive assessments.

The approach to water management worldwide is currently in transition, with a shift from centralised infrastructures to greater consideration of decentralised technologies, such as rainwater harvesting (RWH). Initiated by recognition of drivers, including water demand, increasing risk of ground-water pollution and flooding, the value of RWH is filtering across the academic-policy boundary. However, in Nigeria, implementation of sustainable water management (SWM), such as RWH systems, is inefficient social, environmental and technical barriers, concerns and knowledge gaps exist, which currently restrict its widespread utilisation. This inefficiency contributes to water scarcity, water-borne diseases, and loss of lives and property due to flooding. Meanwhile, several RWH technologies have been developed to improve SWM through both demand and storm-water management. Such technologies involve the use of storage tanks, surface water reservoirs and ground-water recharge pits as storage systems. A framework was developed to assess the significance and extent of water management problems, match the problems with existing RWH-based solutions and develop a robust ready-to-use multi-criteria analysis tool that can quantify the costs and benefits of implementing several RWH-based storage systems. The methodology adopted was the mixed method approach, involving a detailed literature review, followed by a questionnaire survey of 1067 household respondents, 135 Nigerian Architects and Civil Engineers and focus group discussion with Stakeholders. A total of 1042 sets of data were collected through a questionnaire survey and analysed using SPSS, Excel and selected statistical methods to derive weightings of the attributes for the tool. Following this, three case studies were selected to collect data for hydrological modelling using the RainCycle model. From the results it is found that the most important barrier constraining sustainable RWH regime in Ibadan was obsolete and insufficient operational equipment, followed by poor renumeration of water corporation staff and misuse of available funds. In addition, the measure of importance of storage capacity was established, with the highest score of 4.5 which reflects the general inadequacy of storage as a major barrier to the adoption of RWH as a sustainable water management method. Further, respondents’ major health hazards associated with drinking contaminated water was established. A larger proportion (61.2%) of respondents chose prevalence of typhoid fever; some have a prevalence of diarrhea (19.4%), while few of respondents’ water sources is free from water-borne diseases (2.3%). The tool developed is an integrated platform of related evaluation techniques, including Whole Life Cycle Cost Analysis and Multi-Attribute Utility Theory. The tool uses data including cost and quantities of materials for building a RWH storage system and quantifies the cost and benefits of alternative RWH-based systems that can improve project management. This tool is novel, given its integration of the analytical techniques mentioned above and application for selecting the most appropriate RWH-based SWM systems. The implementation of the tool is envisaged to provide an objective platform for the quantification of the costs and benefits of RWH-based systems prior to implementation.