Fuelling the zero-emissions road freight of the future: routing of mobile fuellers

The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios

Quantification et propagation d'incertitude dans les phases amont de projets de conception d'avions : de l'optimisation déterministe à l'optimisation sous contraintes probabilistes

Le " Conceptual Design " est la première étape d'un projet d'avion de transport de passagers. Classiquement, au cours de ce processus, un grand nombre de configurations possibles sont comparées après avoir été dimensionnées sur la base d'un processus d'optimisation déterministe, multidisciplinaire sous contraintes. L'objectif est de définir les paramètres principaux de l'avion qui répondent à un cahier des charges donné de haut niveau. A ce stade du projet, les ingénieurs doivent résoudre le problème en ayant très peu de connaissances sur le produit final et donc beaucoup d'incertitude. La gestion de l'incertitude est un point crucial : réussir à comprendre au plus tôt l'impact qu'elle aura sur la configuration et les performances de l'avion peut permettre de choisir les configurations qui présentent le meilleur rapport bénéfice sur risque ainsi que de réduire le temps de conception ultérieur et donc les coûts. Cette thèse introduit une nouvelle méthodologie pour la résolution d'un problème d'optimisation de configuration avion affecté par de l'incertitude. Dans un premier temps, la source principale d'incertitude présente à ce stade du projet est identifiée comme étant de l'incertitude de prédiction des modèles de simulation. Cette incertitude est de type épistémique. Elle est quantifiée à l'aide d'outils probabilistes. Pour ce faire, et en nous inspirant de la loi Béta, nous avons créé une nouvelle loi de probabilité générique, capable de s'ajuster à des distributions de formes très différentes, intitulée distribution Beta-Mystique. Dans un second temps, nous réalisons des études de propagation d'incertitudes à l'aide des méthodes de Monte Carlo et de propagation des moments, afin d'analyser la robustesse d'une configuration avion par rapport à une quantité d'incertitude donnée. Enfin, une optimisation sous contraintes probabilistes est résolue afin de générer des configurations avions robustes. Deux stratégies sont mises en place : l'approximation des contraintes probabilistes à l'aide de surfaces de réponses et la résolution du problème à l'aide de la méthode de propagation des momentsConceptual aircraft sizing is the first step in the development project of a passenger transport aircraft. Classically, in this phase, a lot of aircraft configurations are compared after having been globally sized thanks to a deterministic, multidisciplinary and constrained optimisation problem. The purpose is to determine the main characteristics of the airplane according to a set of Top Level Requirements. At preliminary stage, designers have to deal with limited knowledge and high uncertainty when solving this problem. Managing that uncertainty is a major issue: assessing its impact on the design in the early stage allows to save time and cost. This PhD thesis introduces a new methodology to solve the aircraft design optimisation affected by uncertainty. First of all, the main source of uncertainty involved at this stage is identified as predictive model uncertainty, which is part of epistemic uncertainty. This uncertainty is quantified within a probabilistic framework. For that purpose, based on the Beta distribution, we create a new generic distribution function able to assume a wide range of distribution shapes: it is called Beta-Mystique distribution. Second of all, we realise uncertainty propagation studies with Monte Carlo and moment propagation methods, in order to analyse the robustness of aircraft configuration according to a set of uncertainties. Finally, a chance constrained optimisation is solved to produce a robust aircraft configuration. Two strategies are considered: the use of Surrogate models to approximate the probabilities and the resolution of the optimisation problem thanks to the moment propagation metho

Distributed Coordination and Optimisation of Network-Aware Electricity Prosumers

Electricity networks are undergoing a transformation brought on by new technologies, market pressures and environmental concerns. This includes a shift from large centralised generators to small-scale distributed generators. The dramatic cost reductions in rooftop solar PV and battery storage means that prosumers (houses and other entities that can both produce and consume electricity) will have a large role to play in future networks. How can networks be managed going forward so that they run as efficiently as possible in this new prosumer paradigm? Our vision is to treat prosumers as active participants by developing a mechanism that incentivises them to help balance power and support the network. The whole process is automated to produce a near-optimal outcome and to reduce the need for human involvement. The first step is to design an autonomous energy management system (EMS) that can optimise the local costs of each prosumer in response to network electricity prices. In particular, we investigate different optimisation strategies for an EMS in an uncertain household environment. We find that the uncertainty associated with weather, network pricing and occupant behaviour can be effectively handled using online optimisation techniques using a forward receding horizon. The next step is to coordinate the actions of many EMSs spread out across the network, in order to minimise the overall cost of supplying electricity. We propose a distributed algorithm that can efficiently coordinate a network with thousands of prosumers without violating their privacy. We experiment with a range of power flow models of varying degrees of accuracy in order to test their convergence rate, computational burden and solution quality on a suburb-sized microgrid. We find that the higher accuracy model, although non-convex, converges in a timely manner and produces near-optimal solutions. We also develop simple but effective techniques for dealing with residential shiftable loads which require discrete decisions. The final part of the problem we explore is prosumer manipulation of the coordination mechanism. The receding horizon nature of our algorithm is great for managing uncertainty, but it opens up unique opportunities for prosumers to manipulate the actions of others. We formalise this form of receding horizon manipulation and investigate the benefits manipulative agents can obtain. We find that indeed strategic agents can harm the system, but only if they are large enough and have information about the behaviour of other agents. For the rare cases where this is possible, we develop simple privacy-preserving identifiers that monitor agents and distinguish manipulation from uncertainty. Together, these components create a complete solution for the distributed coordination and optimisation of network-aware electricity prosumers

A Statistical Approach to the Alignment of fMRI Data

Multi-subject functional Magnetic Resonance Image studies are critical. The anatomical and functional structure varies across subjects, so the image alignment is necessary. We define a probabilistic model to describe functional alignment. Imposing a prior distribution, as the matrix Fisher Von Mises distribution, of the orthogonal transformation parameter, the anatomical information is embedded in the estimation of the parameters, i.e., penalizing the combination of spatially distant voxels. Real applications show an improvement in the classification and interpretability of the results compared to various functional alignment methods

Random projection ensemble classification

We introduce a very general method for high-dimensional classification, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. In one special case that we study in detail, the random projections are divided into disjoint groups, and within each group we select the projection yielding the smallest estimate of the test error. Our random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment. Our theoretical results elucidate the effect on performance of increasing the number of projections. Moreover, under a boundary condition implied by the sufficient dimension reduction assumption, we show that the test excess risk of the random projection ensemble classifier can be controlled by terms that do not depend on the original data dimension and a term that becomes negligible as the number of projections increases. The classifier is also compared empirically with several other popular high-dimensional classifiers via an extensive simulation study, which reveals its excellent finite-sample performance.Both authors are supported by an Engineering and Physical Sciences Research Council Fellowship EP/J017213/1; the second author is also supported by a Philip Leverhulme prize