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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

A multi-objective evolutionary approach to simulation-based optimisation of real-world problems

Syberfeldt, Anna January 2009 (has links)
This thesis presents a novel evolutionary optimisation algorithm that can improve the quality of solutions in simulation-based optimisation. Simulation-based optimisation is the process of finding optimal parameter settings without explicitly examining each possible configuration of settings. An optimisation algorithm generates potential configurations and sends these to the simulation, which acts as an evaluation function. The evaluation results are used to refine the optimisation such that it eventually returns a high-quality solution. The algorithm described in this thesis integrates multi-objective optimisation, parallelism, surrogate usage, and noise handling in a unique way for dealing with simulation-based optimisation problems incurred by these characteristics. In order to handle multiple, conflicting optimisation objectives, the algorithm uses a Pareto approach in which the set of best trade-off solutions is searched for and presented to the user. The algorithm supports a high degree of parallelism by adopting an asynchronous master-slave parallelisation model in combination with an incremental population refinement strategy. A surrogate evaluation function is adopted in the algorithm to quickly identify promising candidate solutions and filter out poor ones. A novel technique based on inheritance is used to compensate for the uncertainties associated with the approximative surrogate evaluations. Furthermore, a novel technique for multi-objective problems that effectively reduces noise by adopting a dynamic procedure in resampling solutions is used to tackle the problem of real-world unpredictability (noise). The proposed algorithm is evaluated on benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of a production cell at Volvo Aero, while the second one concerns the optimisation of a camshaft machining line at Volvo Cars Engine. The results from the optimisations show that the algorithm finds better solutions for all the problems considered than existing, similar algorithms. The new techniques for dealing with surrogate imprecision and noise used in the algorithm are identified as key reasons for the good performance.
2

Optimisation of the VARTM process

Struzziero, Giacomo January 2014 (has links)
This study focuses on the development of a multi-objective optimisation methodology for the vacuum assisted resin transfer moulding composite processing route. Simulations of the cure and filling stages of the process have been implemented and the corresponding heat transfer and flow through porous media problems solved by means of finite element analysis. The simulations involved material sub-models to describe thermal properties, cure kinetics and viscosity evolution. A Genetic algorithm which constitutes the foundation for the development of the optimisation has been adapted, implemented and tested in terms of its effectiveness using four benchmark problems. Two methodologies suitable for multi-objective optimisation of the cure and filling stages have been specified and successfully implemented. In the case of the curing stage the optimisation aims at finding a cure profile minimising both process time and temperature overshoot within the part. In the case of the filling stage the thermal profile during filling, gate locations and initial resin temperature are optimised to minimise filling time and final degree of cure at the end of the filling stage. Investigations of the design landscape for both curing and filling stage have indicated the complex nature of the problems under investigation justifying the choice for using a Genetic algorithm. Application of the two methodologies showed that they are highly efficient in identifying appropriate process designs and significant improvements compared to standard conditions are feasible. In the cure process an overshoot temperature reduction up to 75% in the case of thick component can be achieved whilst for a thin part a 60% reduction in process time can be accomplished. In the filling process a 42% filling time reduction and 14% reduction of degree of cure at the end of the filling can be achieved using the optimisation methodology. Stability analysis of the set of solutions for the curing stage has shown that different degrees of robustness are present among the individuals in the Pareto front. The optimisation methodology has also been integrated with an existing cost model that allowed consideration of process cost in the optimisation of the cure stage. The optimisation resulted in process designs that involve 500 € reduction in process cost. An inverse scheme has been developed based on the optimisation methodology aiming at combining simulation and monitoring of the filling stage for the identification of on-line permeability during an infusion. The methodology was tested using artificial data and it was demonstrated that the methodology is able to handle levels of noise from the measurements up to 5 s per sensor without affecting the quality of the outcome.
3

Multi-objective portfolio optimisation of upstream petroleum projects.

Aristeguieta Alfonzo, Otto D. January 2008 (has links)
The shareholders of E&P companies evaluate the future performance of these companies in terms of multiple performance attributes. Hence, E&P decision makers have the task of allocating limited resources to available project proposals to deliver the best performance on these various attributes. Additionally, the performance of these proposals on these attributes is uncertain and the attributes of the various proposals are usually correlated. As a result of the above, the E&P portfolio optimisation decision setting is characterised by multiple attributes with uncertain future performance. Most recent contributions in the E&P portfolio optimisation arena seek to adapt modern financial portfolio theory concepts to the E&P project portfolio selection problem. These contributions generally focus on understanding the tradeoffs between risk and return for the attribute NPV while acknowledging the presence of correlation among the assets of the portfolio. The result is usually an efficient frontier where one objective is set over the expected value of the NPV and the other is set over a risk metric calculated from the same attribute where, typically, the risk metric has a closed form solution (e.g., variance, standard deviation, semi-standard deviation). However, this methodology fails to acknowledge the presence of multiple attributes in the E&P decision setting. To fill this gap, this thesis proposes a decision support model to optimise risk and return objectives extracted from the NPV attribute and from other financial and/or operational attributes simultaneously. The result of this approach is an approximate Pareto front that explicitly shows the tradeoffs among these objectives whilst honouring intra-project and inter-project correlations. Intra-project correlations are incorporated into the optimisation by integrating the single project models to the portfolio model to be optimised. Inter-project correlation is included by modelling of the oil price a global variable. Additionally, the model uses a multi-objective simulation-optimisation approach and hence it overcomes the need of using risk metrics with closed form solutions. The model is applied to a set of realistic hypothetical offshore E&P projects. The results show the presence of complex relationships among the objectives in the approximate Pareto set. The ability of the method to unveil these relationships hopes to bring more insight to the decision makers and hence promote better investment decisions in the E&P industry. / http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1320463 / Thesis (M.Eng.Sc.) -- University of Adelaide, Australian School of Petroleum, 2008
4

Contribution au Développement de Transport Vert : Proposition d'un Plan de Recharge par Segments des Véhicules Électriques : Étude d'un problème de Tournées de Véhicules Mixtes / Contribution to the Development of Green Transport : Proposal of a Recharging Plan by Segments for Electric Vehicles : Study of a Mix Vehicle Routing Problem

Mouhrim, Nisrine 09 March 2019 (has links)
La mise en oeuvre des véhicules électriques dans le secteur du transport de fret présente une solution durable qui répond aux objectifs environnementaux et économiques. Cette thèse s'oriente dans cette direction, elle porte sur l'étude des problèmes de transport électrique selon deux niveaux décisionnels à savoir le niveau stratégique et opérationnel.Au niveau stratégique, nous traitons le problème d'allocation des segments de recharge d'un véhicule électrique par des ondes électromagnétiques. Pour cela, nous proposons une modélisation du problème sous forme de programme mathématique mixte en nombre entier qui tient compte de la particularité du réseau routier et du véhicule. L'objectif est de déterminer; dans un réseau qui se compose de plusieurs chemins; une allocation stratégique qui constitue un compromis entre le coût d'achat du matériel de recharge et le coût de la batterie en satisfaisant un ensemble de contraintes liées au fonctionnement du système lors de l'exploitation et qui garantissent l'arrivée du véhicule à sa destination sans rupture de charge. Ainsi, nous montrons l'utilité de nos travaux dans un contexte industriel à travers le projet 'Green Truck'. Ce projet consiste à remplacer les camions à combustion par les camions électriques; adapté à la technologie d'alimentation par induction; dans la zone industrialo-portuaire du Havre. Dans cette optique et dans un premier temps, nous traitons le problème d'installation des segments de recharge dynamique. Dans un deuxième temps, nous intégrons le mode de rechargement statique dans la stratégie d'allocation. Nous adoptons la version multi-objective de l'algorithme d'optimisation par essaim de particules pour résoudre le problème. En effet, l'algorithme a montré sa robustesse et son efficacité vis-à-vis de problèmes d'optimisation non-linéaires. Après la linéarisation de notre modèle, nous comparons les résultats obtenus avec ceux issus à partir du solveur CPLEX. Nous montrons la validité des résultats obtenus à travers leur analyse et leur discussion.Au niveau opérationnel, nous étudions le problème de tournées de véhicules dans le cas d'une flott( mixte composée de véhicules électriques et à combustion, ce qui est un véritable réseau industrie rencontré dans la pratique. La particularité de notre travail réside dans la considération du cas où le émissions sont limitées par un système de plafonnement d'émissions pour les véhicule conventionnels. Afin de résoudre le modèle mathématique que nous avons élaboré, nous avons indu trois heuristiques dans l'algorithme SPEA-II qui répondent aux contraintes engendrées par la batterie limitée des véhicules électriques. Après l'analyse des performances de l'algorithme résultant, nou, concluons que l'approche de résolution permet d'achever des résultats compétitifs. / The implementation of electric vehicles in the freight transport sector presents a sustainable solution that meets environmental and economic objectives. This thesis is oriented in this direction, it deals with the study of the problems of electric transportation according to two decisional levels namely the strategic and operational levels.At the strategic level, we study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle's routing. Thus, we show the utility of our work in an industrial context through the 'Green Truck' project. This project consists of replacing diesel trucks by inductive trucks in the industrial-port area of Le Havre. Initially, we are dealing with the problem of allocation of dynamic charging segments. In a second step, we integrate the static reload mode in the allocation strategy. We adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. To assess the quality of solutions generated by the PSO algorithm, the problem is transformed into an equivalent linear programming problem and solved with CPLEX optimizer. The results are analyzed and discussed in order to point out the efficiency of our resolution method.At the operational level, we study a new version of the vehicle routing problem with a mix fleet of electric and combustion vehicles, which is a real industrial network encountered in practice. The particularity of our work lies in the consideration of the case where emissions are limited by an emission cap system for conventional vehicles. In order to solve the mathematical model that we have developed, we have included three heuristics in the SPEA-II algorithm that respond to the constraints generated by the limited battery of electric vehicles. After analyzing the performance of the resulting algorithm, we conclude that the resolution approach achieves competitive results.
5

Artificial intelligence techniques for flood risk management in urban environments

Sayers, William Keith Paul January 2015 (has links)
Flooding is an important concern for the UK, as evidenced by the many extreme flooding events in the last decade. Improved flood risk intervention strategies are therefore highly desirable. The application of hydroinformatics tools, and optimisation algorithms in particular, which could provide guidance towards improved intervention strategies, is hindered by the necessity of performing flood modelling in the process of evaluating solutions. Flood modelling is a computationally demanding task; reducing its impact upon the optimisation process would therefore be a significant achievement and of considerable benefit to this research area. In this thesis sophisticated multi-objective optimisation algorithms have been utilised in combination with cutting-edge flood-risk assessment models to identify least-cost and most-benefit flood risk interventions that can be made on a drainage network. Software analysis and optimisation has improved the flood risk model performance. Additionally, artificial neural networks used as feature detectors have been employed as part of a novel development of an optimisation algorithm. This has alleviated the computational time-demands caused by using extremely complex models. The results from testing indicate that the developed algorithm with feature detectors outperforms (given limited computational resources available) a base multi-objective genetic algorithm. It does so in terms of both dominated hypervolume and a modified convergence metric, at each iteration. This indicates both that a shorter run of the algorithm produces a more optimal result than a similar length run of a chosen base algorithm, and also that a full run to complete convergence takes fewer iterations (and therefore less time) with the new algorithm.
6

Joint minimization of power and delay in wireless access networks / Minimisation conjointe de la puissance et du délai dans les réseaux d’accès sans-fil

Moety, Farah 04 December 2014 (has links)
Dans les réseaux d'accès sans fil, l'un des défis les plus récents est la réduction de la consommation d'énergie du réseau, tout en préservant la qualité de service perçue par les utilisateurs finaux. Cette thèse propose des solutions à ce problème difficile considérant deux objectifs, l'économie d'énergie et la minimisation du délai de transmission. Comme ces objectifs sont contradictoires, un compromis devient inévitable. Par conséquent, nous formulons un problème d’optimisation multi-objectif dont le but est la minimisation conjointe de la puissance consommée et du délai de transmission dans les réseaux sans-fil. La minimisation de la puissance est réalisée en ajustant le mode de fonctionnement des stations de base (BS) du réseau d’un niveau élevé de puissance d’émission vers un niveau d'émission plus faible ou même en mode veille. La minimisation du délai de transmission est réalisée par le meilleur rattachement des utilisateurs avec les BS du réseau. Nous couvrons deux réseaux sans-fil différents en raison de leur pertinence : les réseaux locaux sans-fil (IEEE 802.11 WLAN) et les réseaux cellulaires dotés de la technologie LTE. / In wireless access networks, one of the most recent challenges is reducing the power consumption of the network, while preserving the quality of service perceived by the end users. The present thesis provides solutions to this challenging problem considering two objectives, namely, saving power and minimizing the transmission delay. Since these objectives are conflicting, a tradeoff becomes inevitable. Therefore, we formulate a multi-objective optimization problem with aims of minimizing the network power consumption and transmission delay. Power saving is achieved by adjusting the operation mode of the network Base Stations (BSs) from high transmit power levels to low transmit levels or even sleep mode. Minimizing the transmission delay is achieved by selecting the best user association with the network BSs. We cover two different wireless networks, namely IEEE 802.11 wireless local area networks and LTE cellular networks.

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