Additive Manufacturing (AM) has demonstrated great potential to advance product design and manufacturing, and has showed higher flexibility than conventional manufacturing techniques for the production of small volume, complex and customised components. In an economy focused on the need to develop customised and hi-tech products, there is increasing interest in establishing AM technologies as a more efficient production approach for high value products such as aerospace and biomedical products. Nevertheless, the use of AM processes, for even small to medium volume production faces a number of issues in the current state of the technology. AM production is normally used for making parts with complex geometry which implicates the assessment of numerous processing options or choices; the wrong choice of process parameters can result in poor surface quality, onerous manufacturing time and energy waste, and thus increased production costs and resources. A few commonly used AM processes require the presence of cellular support structures for the production of overhanging parts. Depending on the object complexity their removal can be impossible or very time (and resources) consuming. Currently, there is a lack of tools to advise the AM operator on the optimal choice of process parameters. This prevents the diffusion of AM as an efficient production process for enterprises, and as affordable access to democratic product development for individual users. Research in literature has focused mainly on the optimisation of single criteria for AM production. An integrated predictive modelling and optimisation technique has not yet been well established for identifying an efficient process set up for complicated products which often involve critical building requirements. For instance, there are no robust methods for the optimal design of complex cellular support structures, and most of the software commercially available today does not provide adequate guidance on how to optimally orientate the part into the machine bed, or which particular combination of cellular structures need to be used as support. The choice of wrong support and orientation can degenerate into structure collapse during an AM process such as Selective Laser Melting (SLM), due to the high thermal stress in the junctions between fillets of different cells. Another issue of AM production is the limited parts’ surface quality typically generated by the discrete deposition and fusion of material. This research has focused on the formation of surface morphology of AM parts. Analysis of SLM parts showed that roughness measured was different from that predicted through a classic model based on pure geometrical consideration on the stair step profile. Experiments also revealed the presence of partially bonded particles on the surface; an explanation of this phenomenon has been proposed. Results have been integrated into a novel mathematical model for the prediction of surface roughness of SLM parts. The model formulated correctly describes the observed trend of the experimental data, and thus provides an accurate prediction of surface roughness. This thesis aims to deliver an effective computational methodology for the multi- objective optimisation of the main building conditions that affect process efficiency of AM production. For this purpose, mathematical models have been formulated for the determination of parts’ surface quality, manufacturing time and energy consumption, and for the design of optimal cellular support structures. All the predictive models have been used to evaluate multiple performance and costs objectives; all the objectives are typically contrasting; and all greatly affected by the part’s build orientation. A multi-objective optimisation technique has been developed to visualise and identify optimal trade-offs between all the contrastive objectives for the most efficient AM production. Hence, this thesis has delivered a decision support system to assist the operator in the "process planning" stage, in order to achieve optimal efficiency and sustainability in AM production through maximum material, time and energy savings.

Mobile cloud computing is a way of delegating complex algorithms from a mobile device to the cloud to complete the tasks quickly and save energy on the mobile device. However, the cloud may not be available or suitable for helping all the time. For example, in a battlefield scenario, the cloud may not be reachable. This work considers neighbouring devices as alternatives to the cloud for offloading computation and presents three key contributions, namely a comprehensive investigation of the trade-off between computation and communication, Multi-Objective Optimisation based approach to offloading, and Queuing Theory based algorithms that present the benefits of offloading to neighbours. Initially, the states of neighbouring devices are considered to be known and the decision of computation offloading is proposed as a multi-objective optimisation problem. Novel Pareto optimal solutions are proposed. The results on a simulated dataset show up to 30% increment in performance even when cloud computing is not available. However, information about the environment is seldom known completely. In Chapter 5, a realistic environment is considered such as delayed node state information and partially connected sensors. The network of sensors is modelled as a network of queues (Open Jackson network). The offloading problem is posed as minimum cost problem and solved using Linear solvers. In addition to the simulated dataset, the proposed solution is tested on a real computer vision dataset. The experiments on the random waypoint dataset showed up to 33% boost on performance whereas in the real dataset, exploiting the temporal and spatial distribution of the targets, a significantly higher increment in performance is achieved.

Due to the significant effects on the performance and competitiveness of aircraft, high lift devices are of extreme importance in aircraft design. The flow physics of high lift devices is so complex, that traditional one pass and multi-pass design approaches can’t reach the most optimised concept and multi-objective design optimisation (MDO) methods are increasingly explored in relation to this design task. The accuracy of the optimisation, however, depends on the accuracy of the underlying Computational Fluid Dynamics (CFD) solver. The complexity of the flow around high-lift configuration, namely transition and separation effects leads to a substantial uncertainty associated with CFD results. Particularly, the uncertainty related to the turbulence modelling aspect of the CFD becomes important. Furthermore, employing full viscous flow solvers within MDO puts severe limitations on the density of computational meshes in order to achieve a computationally feasible solution, thereby adding to the uncertainty of the outcome. This thesis explores the effect of uncertainties in CFD modelling when detailed aerodynamic analysis is required in computational design of aircraft configurations. For the purposes of this work, we select the benchmark NLR7301 multi-element airfoil (main wing and flap). This flow around this airfoil features all challenges typical for the high-lift configurations, while at the same time there is a wealth of experimental and computational data available in the literature for this case. A benchmark shape bi-objective optimization problem is formed, by trying to reveal the trade-off between lift and drag coefficients at near stall conditions. Following a detailed validation and grid convergence study, three widely used turbulence models are applied within Reynolds-Averaged Navier-Stokes (RANS) approach. K- Realizable, K- SST and Spalart-Allmaras. The results show that different turbulent models behave differently in the optimisation environment, and yield substantially different optimised shapes, while maintaining the overall optimisation trends (e.g. tendency to maximise camber for the increased lift). The differences between the models however exhibit systemic trends irrespective of the criteria for the selection of the target configuration in the Pareto front. A-posteriori error analysis is also conducted for a wide range of configurations of interest resulting from the optimisation process. Whereas Spalart-Allmaras exhibits best accuracy for the datum airfoil, the overall arrangement of the results obtained with different models in the (Lift, Drag) plane is consistent for all optimisation scenarios leading to increased confidence in the MDO/RANS CFD coupling.

ENGLISH ABSTRACT: It is very seldom that a decision-making problem concerns only a single value or objective. The process of simultaneously optimising two or more con icting objectives is known as multi-objective optimisation (MOO). A number of metaheuristics have been successfully adapted for MOO. The aim of this study was to investigate the feasibility of applying an agent-based modelling approach to MOO. The (s; S) inventory problem was chosen as the application eld for this approach and Anylogic used as model platform. Agents in the model were responsible for inventory and sales management, and had to negotiate with each other in order to nd optimal reorder strategies. The introduction of concepts such as agent satisfaction indexes, aggression factors, and recollection ability guided the negotiation process between the agents. The results revealed that the agents had the ability to nd good strategies. The Pareto front generated from their proposed strategies was a good approximation to the known front. The approach was also successfully applied to a recognised MOO test problem proving that it has the potential to solve a variety of MOO problems. Future research could focus on further developing this approach for more practical applications such as complex supply chain systems, nancial models, risk analysis and economics. / AFRIKAANSE OPSOMMING: Daar is weinig besluitnemingsprobleme waar slegs 'n enkele waarde of doelwit ter sprake is. Die proses waar twee of meer doelwitte, wat in konflik staan met mekaar, gelyktydig optimiseer word, staan bekend as multi-doelwit optimisering (MOO). 'n Aantal metaheuristieke is al suksesvol aangepas vir MOO. Die doelwit van hierdie studie was om ondersoek in te stel na die lewensvatbaarheid van die toepassing van 'n agent gebasseerde modelerings benadering tot MOO. As toepassingsveld vir hierdie benadering was die (s; S) voorraad probleem gekies en Anylogic was gebruik as model platform. In die model was agente verantwoordelik vir voorraad- en verkope bestuur. Hulle moes onderling met mekaar onderhandel om die optimale bestelling strategiee te verkry. Konsepte soos agentbevrediging, aggressie faktore en herinneringsvermoens is ingestel om die onderhandeling tussen die agente te bewerkstellig. Die resultate het gewys dat die agente oor die vermoe beskik om met goeie strategiee vorendag te kom. Die Pareto fronte wat gegenereer is deur hul voorgestelde strategiee was 'n goeie benadering tot die bekende front. Die benadering was ook suksesvol toegepas op 'n erkende MOO toets-probleem wat bewys het dat dit oor die potensiaal beskik om 'n verskeidenheid van MOO probleme op te los. Toekomstige navorsing kan daarop fokus om hierdie benadering verder te ontwikkel vir meer praktiese toepassings soos komplekse voorsieningskettingstelsels, finnansiele modelle, risiko-analises en ekonomie.

For many decisions validation of their outcomes is invariably problematic to objectively assess. Therefore to aid analysis and validation of decision outcomes, approaches which provide improved traceability and more semantically meaningful measurements of the decision process are required. Hence, this research investigates traceability, transparency, interactivity and auditability to improve the decision making process. Approaches and evaluation measures are proposed to facilitate a richer decision making experience. Multi-Criteria Decision Analysis (MCDA) seeks to determine the suitability of alternatives of a goal with respect to multiple criteria. A key component of prominent MCDA methods is the concept of pairwise comparison. For a set of elements, pairwise comparison enables an accurate and transparent extraction and codification of a decision maker’s preferences, though facilitating a separation of concerns. From a set of pairwise comparisons, a ranking of the elements under consideration can be calculated. There are scenarios when a set of pairwise comparisons undergo alteration, both for individual and multiple decision makers. A set of measures of compromise are proposed to quantify the alteration that a set of pairwise comparisons undergo in such scenarios. The measures seek to provide a decision maker with meaningful knowledge regarding how their views have altered. A set of pairwise comparisons may be inconsistent. When inconsistency is present it adversely affects a ranking of the elements derived from the comparisons. Moreover inconsistency within pairwise comparisons used for consideration of more than a handful of elements is almost inevitable. Existing approaches that seek to alter a set of comparisons to reduce inconsistency lack traceability, flexibility, and specific consideration of alteration to the judgments in a way that is meaningful to a decision maker. An approach to inconsistency reduction is proposed that seeks to address these issues. For many decisions the opinions of multiple decision makers are utilized, either to avail of their combined expertise or to incorporate conflicting views. Aggregation of multiple decision makers’ pairwise companions seek to combine the views of the group into a single representation of views. An approach to group aggregation of pairwise comparisons is proposed that models compromise between the decision makers, facilitates decision maker constraints, considers inconsistency reduction during aggregation and dynamically incorporates decision maker weights of importance. With internet access becoming widespread being able to garner the views of a large group of decision makers’ views has become feasible. An approach to the aggregation of a large group of decision makers’ preferences is proposed. The approach facilitates understanding regarding both the agreement and conflict within the group during calculation of an overall group consensus. A Multi-Objective Optimisation Decision Software (MOODS) prototype tool has been developed that implements both the new measures of compromise and the proposed approaches to inconsistency reduction and group aggregation.

Geometrical parameterisation has an important role in the aircraft design process due to its impact on the computational efficiency and accuracy in evaluating different configurations. In the early design stages, an aircraft geometrical model is normally described parametrically with a small number of design parameters which allows fast computation. However, this provides only a course approximation which is generally limited to conventional configurations, where the models have already been validated. An efficient parameterisation method is therefore required to allow rapid synthesis and analysis of novel configurations. Within this context, the main objectives of this research are: 1) Develop an economical geometrical parameterisation method which captures sufficient detail suitable for aerodynamic analysis and optimisation in early design stage, and2) Close the gap between conceptual and preliminary design stages by bringing more detailed information earlier in the design process. Research efforts were initially focused on the parameterisation of two-dimensional curves by evaluating five widely-cited methods for airfoil against five desirable properties. Several metrics have been proposed to measure these properties, based on airfoil fitting tests. The comparison suggested that the Class-Shape Functions Transformation (CST) method is most suitable and therefore was chosen as the two-dimensional curve generation method. A set of blending functions have been introduced and combined with the two-dimensional curves to generate a three-dimensional surface. These surfaces form wing or body sections which are assembled together through a proposed joining algorithm. An object-oriented structure for aircraft components has also been proposed. This allows modelling of the main aircraft surfaces which contain sufficient level of accuracy while utilising a parsimonious number of intuitive design parameters.

Evolutionary Algorithms (EAs) have been widely used for the optimisation of both theoretical and real-world non-linear problems, although such optimisation methods have found reasonably limited utilisation in fields outside of the academic domain. While the causality of this limited uptake in non-academic fields falls outside the scope of this thesis, the core focus of this research remains strongly influenced by the notions of solution feasibility and making optimisation methods more accessible for engineers, both factors attributed to low EA adoption rates in the commercial space. This thesis focuses on the application of bespoke heuristic methods to the field of water distribution system optimisation. Water distribution systems are complex entities that are difficult to model and optimise as they consist of many interacting components each with a set of considerations to address, hence it is important for the engineer to understand and assess the behaviour of the system to enable its effective design and optimisation. The primary goal of this research is to assess the impact that incorporating water systems knowledge into an evolution algorithm has on algorithm performance when applied to water distribution network optimisation problems. This thesis describes the development of two heuristics influenced by the practices of water systems engineers when designing water distribution networks with the view to increasing an algorithm’s performance and resultant solution feasibility. By utilising heuristics based on engineering design principles and integrating them into existing EAs, it is found that both engineering feasibility and general algorithmic performance can be notably improved. Firstly the heuristics are applied to a standard single-objective EA and then to a multi-objective genetic algorithm. The algorithms are assessed on a number of water distribution network benchmarks from the literature including real-world based, large scale systems and compared to the standard variants of the algorithms. Following this, a set of extensive experiments are conducted to explore how the inclusion of water systems knowledge impacts the sensitivity of an evolutionary algorithm to parameter variance. It was found that the performance of both engineering inspired algorithms were less sensitive to parameter change than the standard genetic algorithm variant meaning that non-experts in the field of meta-heuristics will potentially be able to get much better performance out of the engineering heuristic based algorithms without the need for specialist evolutionary algorithm knowledge. In addition this research explores the notion that visualisation techniques can provide water system engineers with a greater insight into the operation and behaviour of an evolutionary algorithm. The final section of this thesis presents a novel three-dimensional representation of pipe based water systems and demonstrates a range of innovative methods to convey information to the user. The interactive visualisation system presented not only allows the engineer to visualise the various parameters of a network but also allows the user to observe the behaviour and progress of an iterative optimisation method. Examples of the combination of the interactive visualisation system and the EAs developed in this work are shown to enable the user to track and visualise the actions of the algorithm. The visualisation aggregates changes to the network over an EA run and grants significant insight into the operations of an EA as it is optimising a network. The research presented in this thesis demonstrates the effectiveness of integrating water system engineering expertise into evolutionary based optimisation methods. Not only is solution quality improved over standard methods utilising these new heuristic techniques, but the potential for greater interaction between engineer, problem and optimiser has been established.

Composites have been increasingly used in different applications in the last decade, especially in aerospace due to their high strength and lightweight characteristics. Indeed, the latest models of Airbus (A350) and Boeing (B787) have employed more than 50 wt% of composites, mainly Carbon Fibre Reinforced Polymers (CFRP). Yet, the increased use of CFRP has raised the environmental concerns about their end-of-life related to waste disposal, consumption of non-renewable resources for manufacturing and the need to recycle CFRP wastes. In this study, a generic model is developed in order to propose an optimal management of aerospace CFRP wastes taking into account economic and environmental objectives. Firstly, a life-cycle systemic approach is used to model the environmental impacts of CFRP recycling processes focusing on Global Warming Potential (GWP) following the guidelines of Life Cycle Assessment (LCA). The whole supply chain for recycling CFRP pathways is then modelled from aircraft dismantling sites to the reuse of recycled fibres in various applications. A multi-objective optimisation strategy based on mathematical programming, -constraint and lexicographic methods with appropriate decisionmaking techniques (M-TOPSIS, PROMETHEE-GAIA) has been developed to determine CFRP waste supply chain configurations. Various scenarios have been studied in order to take account the potential of existing recycling sites in a mono-period visions as well as the deployment of new sites in a multi-period approach considering the case study of France for illustration purpose. The solutions obtained from optimisation process allow developing optimal strategies for the implementation of CFRP recovery with recycled fibres (of acceptable quality) for the targeted substitution use while minimising cost /maximising profit for an economic criterion and minimising an environmental impact based on GWP.

This thesis investigates innovative effluent point-source permitting approaches from an integrated urban wastewater system (UWWS) perspective, and demonstrates that three proposed permitting approaches based on optimal operational or control strategies of the wastewater system are effective in delivering multiple and balanced environmental benefits (water quality, GHG emissions) in a cost-efficient manner. Traditional permitting policy and current flexible permitting practices are first reviewed, and opportunities for permitting from an integrated UWWS perspective are identified. An operational strategy-based permitting approach is first developed by a four-step permitting framework. Based on integrated UWWS modelling, operational strategies are optimised with objectives including minimisation of operational cost, variability of treatment efficiency and environmental risk, subject to compliance of environmental water quality standards. As trade-offs exist between the three objectives, the optimal solutions are screened according to the decision-makers’ preference and permits are derived based on the selected solutions. The advantages of this permitting approach over the traditional regulatory method are: a) cost-effectiveness is considered in decision-making, and b) permitting based on operational strategies is more reliable in delivering desirable environmental outcomes. In the studied case, the selected operational strategies achieve over 78% lower environmental risk with at least 7% lower operational cost than the baseline scenario; in comparison, the traditional end-of-pipe limits can lead to expensive solutions with no better environmental water quality. The developed permitting framework facilitates the derivation of sustainable solutions as: a) stakeholders are involved at all points of the decision-making process, so that various impacts of the operation of the UWWS can be considered, and b) multi-objective optimisation algorithm and visual analytics tool are employed to efficiently optimise and select high performance operational solutions. The second proposed permitting approach is based on optimal integrated real time control (RTC) strategies. Permits are developed by a three-step decision-making analysis framework similar to the first approach. An off-line model-based predictive aeration control strategy is investigated for the case study, and further benefits (9% lower environmental risk and 0.6% less cost) are achieved by an optimal RTC strategy exploiting the dynamic assimilation capacity of the environment. A similar permitting approach, but simpler than the first two methods, is developed to derive operational/control strategy-based permits by an integrated cost-risk analysis framework. Less comprehensive modelling and optimisation skills are needed as it couples a dynamic wastewater system model and a stochastic permitting model and uses sensitivity analysis and scenario analysis to optimise operational/control strategies, hence this approach can be a good option to develop risk-based cost-effective permits without intensive resources. Finally, roadmaps for the implementation of the three innovative permitting approaches are discussed. Current performance-based regulations and self-monitoring schemes are used as examples to visualise the new way of permitting. The viability of the proposed methods as alternative regulation approaches are evaluated against the core competencies of modern policy-making.

Geometrical parameterisation has an important role in the aircraft design process due to its impact on the computational efficiency and accuracy in evaluating different configurations. In the early design stages, an aircraft geometrical model is normally described parametrically with a small number of design parameters which allows fast computation. However, this provides only a course approximation which is generally limited to conventional configurations, where the models have already been validated. An efficient parameterisation method is therefore required to allow rapid synthesis and analysis of novel configurations. Within this context, the main objectives of this research are: 1) Develop an economical geometrical parameterisation method which captures sufficient detail suitable for aerodynamic analysis and optimisation in early design stage, and2) Close the gap between conceptual and preliminary design stages by bringing more detailed information earlier in the design process. Research efforts were initially focused on the parameterisation of two-dimensional curves by evaluating five widely-cited methods for airfoil against five desirable properties. Several metrics have been proposed to measure these properties, based on airfoil fitting tests. The comparison suggested that the Class-Shape Functions Transformation (CST) method is most suitable and therefore was chosen as the two-dimensional curve generation method. A set of blending functions have been introduced and combined with the two-dimensional curves to generate a three-dimensional surface. These surfaces form wing or body sections which are assembled together through a proposed joining algorithm. An object-oriented structure for aircraft components has also been proposed. This allows modelling of the main aircraft surfaces which contain sufficient level of accuracy while utilising a parsimonious number of intuitive design parameters ... [cont.].