Simulating Systems-of-Systems Dynamic Architectures

Systems-of-Systems (SoS) combine heterogeneous, independent systems to offer complex functionalities for highly dynamic smart applications. Due to their critical nature, SoS should be reliable and work without interruption that could cause serious losses. SoS architectural design can facilitate the prediction of the impact of failures due to SoS behavior. However, existing approaches do not support such evaluation. The main contribution of this paper is to present Dynamic-SoS, an approach to predict, at design time, the SoS architectural behavior at runtime to evaluate whether the SoS can sustain their operation. Results of our multiple case studies reveal Dynamic-SoS is a promising approach that could contribute to the quality of SoS by reliably enabling prior assessment of their dynamic architecture

Understanding the Elements of Executable Architectures Through a Multi-Dimensional Analysis Framework

The objective of this dissertation study is to conduct a holistic investigation into the elements of executable architectures. Current research in the field of Executable Architectures has provided valuable solution-specific demonstrations and has also shown the value derived from such an endeavor. However, a common theory underlying their applications has been missing. This dissertation develops and explores a method for holistically developing an Executable Architecture Specification (EAS), i.e., a meta-model containing both semantic and syntactic information, using a conceptual framework for guiding data coding, analysis, and validation. Utilization of this method resulted in the description of the elements of executable architecture in terms of a set of nine information interrogatives: an executable architecture information ontology. Once the detail-rich EAS was constructed with this ontology, it became possible to define the potential elements of executable architecture through an intermediate level meta-model. The intermediate level meta-model was further refined into an interrogative level meta-model using only the nine information interrogatives, at a very high level of abstraction

Computer-aided design for building multipurpose routing processes in discrete event simulation models

Good domain-modeling enables an appropriate separation of concerns that improves quality properties in the simulation models, such as modifiability and maintainability. In this paper, the interplay of abstraction and concreteness in advancing the theory and practice of Modelling and Simulation is improved using the Model-Driven Engineering levels for building simulation models devoted to routing processes. The definition of this type of processes is detailed as a domain-model conceived as an abstraction defined in a graph model. Such abstraction turns into a set of formal simulation models that are (later) translated into an executable implementation. The final simulation models are specified using Routed DEVS formalism. The methodological proposal is accomplished with the development of a Modelling and Simulation graphical software tool that uses the set of models (defined in terms of the Model-Driven Engineering approach) as the core of its operation. This graphical software tool is developed as a plug-in for Eclipse Integrated Development Environment with aims to take advantage of existent Modeling and Simulation software. Therefore, the usefulness of graphical modeling for supporting the development of the simulation models is empowered with a Model-Driven Engineering process. The main benefit obtained when the Model-Driven Engineering approach is used for modeling an abstraction of the final simulation model is a clear reduction of formalization and implementation times.Fil: Blas, María Julia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaFil: Gonnet, Silvio Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentin

Scheduling of a Cyber-Physical System Simulation

The work carried out in this Ph.D. thesis is part of a broader effort to automate industrial simulation systems. In the aeronautics industry, and more especially within Airbus, the historical application of simulation is pilot training. There are also more recent uses in the design of systems, as well as in the integration of these systems. These latter applications require a very high degree of representativeness, where historically the most important factor has been the pilot’s feeling. Systems are now divided into several subsystems that are designed, implemented and validated independently, in order to maintain their control despite the increase in their complexity, and the reduction in time-to-market. Airbus already has expertise in the simulation of these subsystems, as well as their integration into a simulation. This expertise is empirical; simulation specialists use the previous integrations schedulings and adapt it to a new integration. This is a process that can sometimes be time-consuming and can introduce errors. The current trends in the industry are towards flexible production methods, integration of logistics tools for tracking, use of simulation tools in production, as well as resources optimization. Products are increasingly iterations of older, improved products, and tests and simulations are increasingly integrated into their life cycles. Working empirically in an industry that requires flexibility is a constraint, and nowadays it is essential to facilitate the modification of simulations. The problem is, therefore, to set up methods and tools allowing a priori to generate representative simulation schedules. In order to solve this problem, we have developed a method to describe the elements of a simulation, as well as how this simulation can be executed, and functions to generate schedules. Subsequently, we implemented a tool to automate the scheduling search, based on heuristics. Finally, we tested and verified our method and tools in academic and industrial case studies

Understanding the Impact of Large-Scale Power Grid Architectures on Performance

Grid balancing is a critical system requirement for the power grid in matching the supply to the demand. This balancing has historically been achieved by conventional power generators. However, the increasing level of renewable penetration has brought more variability and uncertainty to the grid (Ela, Diakov et al. 2013, Bessa, Moreira et al. 2014), which has considerable impacts and implications on power system reliability and efficiency, as well as costs. Energy planners have the task of designing infrastructure power systems to provide electricity to the population, wherever and whenever needed. Deciding of the right grid architecture is no easy task, considering consumers’ economic, environmental, and security priorities, while making efficient use of existing resources. In this research, as one contribution, we explore associations between grid architectures and their performance, that is, their ability to meet consumers’ concerns. To do this, we first conduct a correlation analysis study. We propose a generative method that captures path dependency by iteratively creating grids, structurally different. The method would generate alternative grid architectures by subjecting an initial grid to a heuristic choice method for decision making over a fixed time horizon. Second, we also conduct a comparative study to evaluate differences in grid performances. We consider two balancing area operation types, presenting different structures and coordination mechanisms. Both studies are performed with the use of a grid simulation model, Spark! The aim of this model is to offer a meso-scale solution that enables the study of very large power systems over long-time horizons, with a sufficient level of fidelity to perform day-to-day grid activities and support architectural questions about the grids of the future. More importantly, the model reconciles long-term planning with short-term grid operations, enabling long-term projections validation via grid operations and response on a daily basis. This is our second contribution

Actor based behavioural simulation as an aid for organisational decision making

Decision-making is a critical activity for most of the modern organizations to stay competitive in rapidly changing business environment. Effective organisational decision-making requires deep understanding of various organisational aspects such as its goals, structure, business-as-usual operational processes, environment where it operates, and inherent characteristics of the change drivers that may impact the organisation. The size of a modern organisation, its socio-technical characteristics, inherent uncertainty, volatile operating environment, and prohibitively high cost of the incorrect decisions make decision-making a challenging endeavor. While the enterprise modelling and simulation technologies have evolved into a mature discipline for understanding a range of engineering, defense and control systems, their application in organisational decision-making is considerably low. Current organisational decision-making approaches that are prevalent in practice are largely qualitative. Moreover, they mostly rely on human experts who are often aided with the primitive technologies such as spreadsheets and visual diagrams. This thesis argues that the existing modelling and simulation technologies are neither suitable to represent organisation and decision artifacts in a comprehensive and machine-interpretable form nor do they comprehensively address the analysis needs. An approach that advances the modelling abstraction and analysis machinery for organisational decision-making is proposed. In particular, this thesis proposes a domain specific language to represent relevant aspects of an organisation for decision-making, establishes the relevance of a bottom-up simulation technique as a means for analysis, and introduces a method to utilise the proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner

A model-based approach to System of Systems risk management

The failure of many System of Systems (SoS) enterprises can be attributed to the inappropriate application of traditional Systems Engineering (SE) processes within the SoS domain, because of the mistaken belief that a SoS can be regarded as a single large, or complex, system. SoS Engineering (SoSE) is a sub-discipline of SE; Risk Management and Modelling and Simulation (M&S) are key areas within SoSE, both of which also lie within the traditional SE domain. Risk Management of SoS requires a different approach to that currently taken for individual systems; if risk is managed for each component system then it cannot be assumed that the aggregated affect will be to mitigate risk at the SoS level. A literature review was undertaken examining three themes: (1) SoS Engineering (SoSE), (2) M&S and (3) Risk. Theme 1 of the literature provided insight into the activities comprising SoSE and its difference from traditional SE with risk management identified as a key activity. The second theme discussed the application of M&S to SoS, providing an output, which supported the identification of appropriate techniques and concluding that, the inherent complexity of a SoS required the use of M&S in order to support SoSE activities. Current risk management approaches were reviewed in theme 3 as well as the management of SoS risk. Although some specific examples of the management of SoS risk were found, no mature, general approach was identified, indicating a gap in current knowledge. However, it was noted most of these examples were underpinned by M&S approaches. It was therefore concluded a general approach SoS risk management utilising M&S methods would be of benefit. In order to fill the gap identified in current knowledge, this research proposed a new model based approach to Risk Management where risk identification was supported by a framework, which combined SoS system of interest dimensions with holistic risk types, where the resulting risks and contributing factors are captured in a causal network. Analysis of the causal network using a model technique selection tool, developed as part of this research, allowed the causal network to be simplified through the replacement of groups of elements within the network by appropriate supporting models. The Bayesian Belief Network (BBN) was identified as a suitable method to represent SoS risk. Supporting models run in Monte Carlo Simulations allowed data to be generated from which the risk BBNs could learn, thereby providing a more quantitative approach to SoS risk management. A method was developed which provided context to the BBN risk output through comparison with worst and best-case risk probabilities. The model based approach to Risk Management was applied to two very different case studies: Close Air Support mission planning and the Wheat Supply Chain, UK National Food Security risks, demonstrating its effectiveness and adaptability. The research established that the SoS SoI is essential for effective SoS risk identification and analysis of risk transfer, effective SoS modelling requires a range of techniques where suitability is determined by the problem context, the responsibility for SoS Risk Management is related to the overall SoS classification and the model based approach to SoS risk management was effective for both application case studies

A model based approach for complex dynamic decision-making

Current state-of-the-practice and state-of-the-art of decision-making aids are inadequate for modern organisations that deal with significant uncertainty and business dynamism. This paper highlights the limitations of prevalent decision-making aids and proposes a model-based approach that advances the modelling abstraction and analysis machinery for complex dynamic decision-making. In particular, this paper proposes a meta-model to comprehensively represent organisation, establishes the relevance of model-based simulation technique as analysis means, introduces the advancements over actor technology to address analysis needs, and proposes a method to utilise proposed modelling abstraction, analysis technique, and analysis machinery in an effective and convenient manner. The proposed approach is illustrated using a near real-life case-study from a business process outsourcing organisation

A framework to study the resilience of organizations: a case study of a nuclear emergency plan

El desarrollo de la resiliencia es un campo de investigación importante en ámbitos como el Management, la Ingeniería, la Psicología o la Ecología. La importancia del estudio de la resiliencia se ha visto desarrollada por el aumento tanto de desastres naturales como antropogénicos, así como por el desarrollo de conciencia acerca de sus efectos. Estas razones de peso han influido en que los Gobiernos estén invirtiendo recursos en la mejora de la resiliencia de organizaciones, infraestructuras, ciudades, individuos, etc. Sin embargo, a pesar de su importancia, el número de trabajos de investigación que se centran en el desarrollo de metodologías específicas para el diseño de organizaciones resilientes es reducido. El principal objetivo de esta investigación es mejorar este aspecto introduciendo un marco para el diseño de organizaciones resilientes. Para alcanzar este objetivo, se explica cómo emplear el Modelo de Sistemas Viables para el diseño de estas organizaciones. Nos hemos centrado en uno de los aspectos clave de la resiliencia: las comunicaciones. Para ello, se ha usado el caso de estudio del plan de emergencia de una central nuclear en España. Las comunicaciones en una organización pueden modelarse como un proceso de difusión en redes multiplex. Buscamos arquitecturas aplicables a nuestro caso de estudio. Sin embargo, no se ha encontrado ninguna que cumpliera con los requisitos que se necesitaban. Este hecho, nos ha llevado a proponer una nueva arquitectura, que además de permitir estudiar la difusión de información en una organización, permite estudiar otros procesos de difusión en redes multiplex.Departamento de Organización de Empresas y Comercialización e Investigación de MercadosDoctorado en Ingeniería Industria