Visual analytics approach to user-controlled evacuation scheduling

Application of the ideas of visual analytics is a promising approach to supporting decision making, in particular, where the problems have geographic (or spatial) and temporal aspects. Visual analytics may be especially helpful in time-critical applications, which pose hard challenges to decision support. We have designed a suite of tools to support transportation-planning tasks such as emergency evacuation of people from a disaster-affected area. The suite combines a tool for automated scheduling based on a genetic algorithm with visual analytics techniques allowing the user to evaluate tool results and direct its work. A transportation schedule, which is generated by the tool, is a complex construct involving geographical space, time, and heterogeneous objects (people and vehicles) with states and positions varying in time. We apply task-analytical approach to design techniques that could effectively support a human planner in the analysis of this complex information

Numerische Methoden für marine biogeochemische Modelle

Marine ecosystem models are an indispensable component in the forecast of climate change. CO2 is the major anthropogenic greenhouse gas which substantially determines global warming. As an essential component of the global carbon cycle, the marine ecosystem absorbs atmospheric CO2 and, hence, slows down global warming. More specifically, the marine ecosystem stores the CO2 over a long time period, for example by fixing it through biogeochemical conversion processes. Marine ecosystem models facilitate the simulation of the marine ecosystem and, thus, the research of different processes in this ecosystem and a forecast of the evolution of the marine ecosystem. Owing to a high computational effort, the simulation of marine ecosystem models is limited by the available computing power, even on high-performance computers. To reduce the computational effort for the computation of a steady annual cycle for a marine ecosystem model, this thesis comprises the investigation of the reduction of the computational effort by using larger time steps and by predicting the steady annual cycle by means of an artificial neural network. To apply the time step always as large as possible without relying on any manual selection, two methods based on the automatic time step adjustment during the simulation are presented. The prediction of an artificial neural network served as an initial concentration for an additional simulation because the accuracy of the prediction was insufficient. These approaches, in particular, lowered the computational effort with a tolerable loss of accuracy. By the use of the surrogate-based optimization, the approaches to reduce the computational effort were applied for a parameter identification which optimizes the model parameters to adapt the marine ecosystem model output to observational data. This optimization yielded parameters close to the target ones and lowered the computational effort clearly.Marine Ökosystemmodelle sind ein unverzichtbarer Bestandteil zur Vorhersage des Klimawandels. Die globale Erwärmung wird im Wesentlichen durch Emissionen des bedeutendsten anthropogenen Treibhausgases Kohlenstoffdioxid (CO2) bestimmt. Als eine zentrale Komponente des globalen Kohlenstoffkreislaufs absorbiert das marine Ökosystem atmosphärisches CO2 und verlangsamt so die globale Erwärmung. Marine Ökosystemmodelle ermöglichen die Simulation und somit die Erforschung verschiedener Prozesse innerhalb des marinen Ökosystems sowie eine Vorhersage der zu erwartenden Entwicklung. Allerdings erfordert eine solche Simulation einen immensen Rechenaufwand und unterliegt selbst auf Hochleistungsrechnern durch die begrenzte Rechenleistung erheblichen Einschränkungen. Für die Berechnung einer jährlich periodischen Lösung des marinen Ökosystemmodells zeigt diese Arbeit Wege zur Reduktion des Rechenaufwands durch die Verwendung größerer Zeitschritte und durch die Vorhersage eines neuronalen Netzes auf. Es werden zwei Methoden vorgestellt, die auf der automatischen Anpassung des Zeitschritts während der Simulation basieren, um ohne manuelle Wahl immer den größtmöglichen Zeitschritt zu verwenden. Die Vorhersage der periodischen Lösung mit Hilfe eines neuronalen Netzes diente als Anfangskonzentration für eine zusätzliche Simulation, da die Genauigkeit der Vorhersage unzureichend war. Beide Ansätze verringerten den Rechenaufwand bei einem tolerierbaren Genauigkeitsverlust. Die Konzepte zur Reduktion des Rechenaufwands wurden für eine Parameteroptimierung mit der surrogat-basierten Optimierung verwendet, die die Modellparameter zur Anpassung des marinen Ökosystemmodells an Beobachtungsdaten optimiert. Diese Optimierung lieferte nahezu die anvisierten Parameter und verringerte den Rechenaufwand

A Computational View on Natural Evolution: On the Rigorous Analysis of the Speed of Adaptation

Inspired by Darwin’s ideas, Turing (1948) proposed an evolutionary search as an automated problem solving approach. Mimicking natural evolution, evolutionary algorithms evolve a set of solutions through the repeated application of the evolutionary operators (mutation, recombination and selection). Evolutionary algorithms belong to the family of black box algorithms which are general purpose optimisation tools. They are typically used when no good specific algorithm is known for the problem at hand and they have been reported to be surprisingly effective (Eiben and Smith, 2015; Sarker et al., 2002). Interestingly, although evolutionary algorithms are heavily inspired by natural evolution, their study has deviated from the study of evolution by the population genetics community. We believe that this is a missed opportunity and that both fields can benefit from an interdisciplinary collaboration. The question of how long it takes for a natural population to evolve complex adaptations has fascinated researchers for decades. We will argue that this is an equivalent research question to the runtime analysis of algorithms. By making use of the methods and techniques used in both fields, we will derive plenty of meaningful results for both communities, proving that this interdisciplinary approach is effective and relevant. We will apply the tools used in the theoretical analysis of evolutionary algorithms to quantify the complexity of adaptive walks on many landscapes, illustrating how the structure of the fitness landscape and the parameter conditions can impose limits to adaptation. Furthermore, as geneticists use diffusion theory to track the change in the allele frequencies of a population, we will develop a brand new model to analyse the dynamics of evolutionary algorithms. Our model, based on stochastic differential equations, will allow to describe not only the expected behaviour, but also to measure how much the process might deviate from that expectation

Genetic-Based Optimisation Technique for the Development of Automated Inspection and Restoration Systems for Bridges

Automation and robotics are receiving significant attention in the field of inspection and restoration of steel bridges. However, the success level of the field implementations depends on numerous technological factors. This dissertation addresses aspects of the design, development and subsequent implementation of such on-site devices. The restoration process poses a high level of health hazard and carries environmental pollution risk. For these reasons, it is high on the consideration list for automation. The varied scale and geometry of bridges are some of the limiting conditions for performing the inspection and restoration tasks. Further aspects of concern are access provisions, the diversity of tasks required in the assessment and restoration of a bridge and compatibility between the operational characteristics of the automated device, tasks layout and direction of approach. The key factors, which arise as a result of the above analysis, are access, mobility, navigation, manipulation, probe change and control. In order to efficiently produce design alternatives, based on the industry (customers and designers) requirements, the engineering design framework is adopted. Due to the growing complexity of the required devices, new methodologies and approaches are needed. This dissertation presents a design methodology to generate alternatives for further considerations. The author's work combines: (i) research and suitability assessment of the existing enabling technologies, (ii) extensive task selection and analysis, (iii) incorporation of the industry requirements for generating the set of design criteria, and (iv) an innovative application of Genetic Algorithms. GA is used as a tool for simultaneous optimisation of the robot’s kinematic parameters, based on the criteria of collision and singularity avoidance, percentage of coverage, productivity and dexterity. Analysis and justification of a two-step approach is presented, with the former combining all the parameters, and the latter handling the chosen criteria. The methodology is then tested and verified on an existing construction robot (MPIR) from Technion. Finally, it is applied to two case studies, spherical and articulated manipulators performing a range of restoration activities on a selected bridge geometry model. A sensitivity analysis was also carried out on each case study in order to identify areas where improvements could be made. In general, the methodology is successful in choosing the more task-suitable manipulator and optimising the ranges of its kinematic parameters. This could be extended to optimise other parameters according to a set of alternative criteria. In doing so, it can bridge over several phases of the engineering design with a single approach

Optimization of large scale parcel distribution systems by the Breeder Genetic Algorithm (BGA)

The routing and scheduling of vehicles and their crews is an area of increasing importance. In this paper, we describe a large scale vehicle scheduling and routing problem which is of great importance to parcel distribution companies. We devise a Breeder Genetic Algorithm capable of dealing with up to 10,000 transportation requests to be serviced by an inhomogeneous fleet of vehicles within a 24 hour time interval. A transportation request is defined as the task to move a loaded container from one depot to another. Since the depots do not send out the same number of containers as they receive, the amount of empty containers available at the depots has to be balanced. The optimization task, therefore, is twofold: determine appropriate balance tours and find a low cost schedule for the fleet of vehicles. 1 INTRODUCTION The routing and scheduling of vehicles and their crews is an area of increasing importance. Often, the problems described in the scientific literature oversimplify the on..