Quantitative Set-Based Design for Complex System Development

This dissertation comprises a body of research facilitating decision-making and complex system development with quantitative set-based design (SBD). SBD is concurrent product development methodology, which develops and analyzes many design alternatives for longer time periods enabling design maturation and uncertainty reduction. SBD improves design space exploration, facilitating the identification of resilient and affordable systems. The literature contains numerous qualitative descriptions and quantitative methodologies describing limited aspects of the SBD process. However, there exist no methodologies enabling the quantitative management of SBD programs throughout the entire product development cycle. This research addresses this knowledge gap by developing the process framework and supporting methodologies guiding product development from initial system concepts to a final design solution. This research provides several new research contributions. First, we provide a comprehensive SBD state-of-practice assessment identifying key knowledge and methodology gaps. Second, we demonstrate the physical implementation of the integrated analytics framework in a model-based engineering environment. Third, we develop a quantitative methodology enabling program management decision making in SBD. Fourth, we describe a supporting uncertainty reduction methodology using multiobjective value of information analysis to assess design set maturity and higher-resolution model usefulness. Finally, we describe a quantitative SBD process framework enabling sequential design maturation and uncertainty reduction decisions. Using an unmanned aerial vehicle case study, we demonstrate our methodology’s ability to resolve uncertainty and converge a complex design space onto a set of resilient and affordable design solutions

Identifying vulnerabilities of industrial control systems using evolutionary multiobjective optimisation

In this paper, we propose a novel methodology to assist in identifying vulnerabilities in real-world complex heterogeneous industrial control systems (ICS) using two Evolutionary Multiobjective Optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well-known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these vulnerabilities to generate combinatorial attacks. The generated attacks were aimed at compromising the safety of the system and inflicting economic loss. Results were compared against random attacks, and the performance of the EMO algorithms was evaluated using hypervolume, spread, and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using machine learning algorithms. The designed approach was further tested against the developed detection methods. The obtained results demonstrate that the developed EMO approach is a promising tool in the identification of the vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can serve as a proactive defense tool for control and security engineers to identify and prioritise vulnerabilities in the system. The approach can be employed to design resilient control strategies and test the effectiveness of security mechanisms, both in the design stage and during the operational phase of the system

Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation

In this paper we propose a novel methodology to assist in identifying vulnerabilities in a real-world complex heterogeneous industrial control systems (ICS) using two evolutionary multiobjective optimisation (EMO) algorithms, NSGA-II and SPEA2. Our approach is evaluated on a well known benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. We identified vulnerabilities in individual components of the TE model and then made use of these to generate combinatorial attacks to damage the safety of the system, and to cause economic loss. Results were compared against random attacks, and the performance of the EMO algorithms were evaluated using hypervolume, spread and inverted generational distance (IGD) metrics. A defence against these attacks in the form of a novel intrusion detection system was developed, using a number of machine learning algorithms. Designed approach was further tested against the developed detection methods. Results demonstrate that EMO algorithms are a promising tool in the identification of the most vulnerable components of ICS, and weaknesses of any existing detection systems in place to protect the system. The proposed approach can be used by control and security engineers to design security aware control, and test the effectiveness of security mechanisms, both during design, and later during system operation.Comment: 25 page

Multiscale Resilience in Water Distribution and Drainage Systems

open access articleMultiscale resilience, i.e., coordinating different scales within a system to jointly cope and mitigate risks on any single scale, is identified as the feature of a complex resilient system. However, in water distribution systems (WDSs) and urban drainage systems (UDSs), the inherent resilience is usually not multiscale resilience. By referring to the larger scale to larger pipes serving both local users and some other users at smaller scales, it can be found that smaller scales are not responsible for providing resilience to cope with failures in larger scales. These are because the main function of traditional water systems is to deliver water from upstream to downstream. This study demonstrates that improving multiscale resilience in WDSs and UDSs needs to allow water to travel reversely in the system via providing extra capacities and/or connections at smaller scales. This hypothesis is verified via case studies on a real world WDS and UDS

Optimización del diseño estructural de pavimentos asfálticos para calles y carreteras

gráficos, tablasThe construction of asphalt pavements in streets and highways is an activity that requires optimizing the consumption of significant economic and natural resources. Pavement design optimization meets contradictory objectives according to the availability of resources and users’ needs. This dissertation explores the application of metaheuristics to optimize the design of asphalt pavements using an incremental design based on the prediction of damage and vehicle operating costs (VOC). The costs are proportional to energy and resource consumption and polluting emissions. The evolution of asphalt pavement design and metaheuristic optimization techniques on this topic were reviewed. Four computer programs were developed: (1) UNLEA, a program for the structural analysis of multilayer systems. (2) PSO-UNLEA, a program that uses particle swarm optimization metaheuristic (PSO) for the backcalculation of pavement moduli. (3) UNPAVE, an incremental pavement design program based on the equations of the North American MEPDG and includes the computation of vehicle operating costs based on IRI. (4) PSO-PAVE, a PSO program to search for thicknesses that optimize the design considering construction and vehicle operating costs. The case studies show that the backcalculation and structural design of pavements can be optimized by PSO considering restrictions in the thickness and the selection of materials. Future developments should reduce the computational cost and calibrate the pavement performance and VOC models. (Texto tomado de la fuente)La construcción de pavimentos asfálticos en calles y carreteras es una actividad que requiere la optimización del consumo de cuantiosos recursos económicos y naturales. La optimización del diseño de pavimentos atiende objetivos contradictorios de acuerdo con la disponibilidad de recursos y las necesidades de los usuarios. Este trabajo explora el empleo de metaheurísticas para optimizar el diseño de pavimentos asfálticos empleando el diseño incremental basado en la predicción del deterioro y los costos de operación vehicular (COV). Los costos son proporcionales al consumo energético y de recursos y las emisiones contaminantes. Se revisó la evolución del diseño de pavimentos asfálticos y el desarrollo de técnicas metaheurísticas de optimización en este tema. Se desarrollaron cuatro programas de computador: (1) UNLEA, programa para el análisis estructural de sistemas multicapa. (2) PSO-UNLEA, programa que emplea la metaheurística de optimización con enjambre de partículas (PSO) para el cálculo inverso de módulos de pavimentos. (3) UNPAVE, programa de diseño incremental de pavimentos basado en las ecuaciones de la MEPDG norteamericana, y el cálculo de costos de construcción y operación vehicular basados en el IRI. (4) PSO-PAVE, programa que emplea la PSO en la búsqueda de espesores que permitan optimizar el diseño considerando los costos de construcción y de operación vehicular. Los estudios de caso muestran que el cálculo inverso y el diseño estructural de pavimentos pueden optimizarse mediante PSO considerando restricciones en los espesores y la selección de materiales. Los desarrollos futuros deben enfocarse en reducir el costo computacional y calibrar los modelos de deterioro y COV.DoctoradoDoctor en Ingeniería - Ingeniería AutomáticaDiseño incremental de pavimentosEléctrica, Electrónica, Automatización Y Telecomunicacione

Combining robustness and recovery in rapid transit network design

When designing a transport network, decisions are made according to an expected value for network state variables, such as infrastructure and vehicle conditions, which are uncertain at a planning horizon of up to decades. Because disruptions, such as infrastructure breakdowns, will arise and affect the network on the day of operations, actions must be taken from the network design. Robust network designs may be implemented but they are extremely expensive if disruptions do not realise. In this paper, we propose a novel approach to the network design problem where robustness and recovery are combined. We look for the trade-off between efficiency and robustness accounting for the possibility of recovering from disruptions: recoverable robust network design. Computational experiments drawn from fictitious and realistic networks show how the presented approach reduces the price of robustness and recovery costs as compared to traditional robust and non-robust rapid transit network design approaches

A framework for improving routing configurations using multi-objective optimization mechanisms

IP networks are nowadays well established technolo- gies being used to support a myriad of applications and services, thus assuming a crucial role in todays telecommunication sys- tems. Nevertheless, such infrastructures usually require network administrators to perform a wide set of complex planning and management tasks trying to attain adequate network configura- tions. Many of such management tasks can be mathematically for- mulated as NP-hard optimization problems, sometimes involving several objective functions. In this context, this work explores and demonstrates the potential of using computational intelligence methods as optimization engines to tackle complex network op- timization problems. In particular, Multi-objective Evolutionary Algorithms (MOEAs) are used to attain near-optimal link state routing configurations robust to distinct operational conditions. As result, network administrators will be provided with a set of alternative routing configurations representing distinct tradeoffs between the considered optimization goals. The robustness of the proposed methods is illustrated by presenting several multi-objective optimization examples able to improve the performance and resilience levels of a network infrastructure. Moreover, the devised methods are integrated in a freely available Traffic Engineering optimization framework able to be used by network administrators interested in this particular research field.This work has been supported by COMPETE: POCI-010145-FEDER-007043 and FCT Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013