A synthesis of logic and biology in the design of dependable systems

The technologies of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, have advanced in recent years. Much of this development can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that combines effectively and throughout the design lifecycle these two techniques which are schematically founded on the two pillars of formal logic and biology. Such a design paradigm would apply these techniques synergistically and systematically from the early stages of design to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems that brings these technologies together to realise their combined potential benefits

A synthesis of logic and bio-inspired techniques in the design of dependable systems

Much of the development of model-based design and dependability analysis in the design of dependable systems, including software intensive systems, can be attributed to the application of advances in formal logic and its application to fault forecasting and verification of systems. In parallel, work on bio-inspired technologies has shown potential for the evolutionary design of engineering systems via automated exploration of potentially large design spaces. We have not yet seen the emergence of a design paradigm that effectively combines these two techniques, schematically founded on the two pillars of formal logic and biology, from the early stages of, and throughout, the design lifecycle. Such a design paradigm would apply these techniques synergistically and systematically to enable optimal refinement of new designs which can be driven effectively by dependability requirements. The paper sketches such a model-centric paradigm for the design of dependable systems, presented in the scope of the HiP-HOPS tool and technique, that brings these technologies together to realise their combined potential benefits. The paper begins by identifying current challenges in model-based safety assessment and then overviews the use of meta-heuristics at various stages of the design lifecycle covering topics that span from allocation of dependability requirements, through dependability analysis, to multi-objective optimisation of system architectures and maintenance schedules

Automating allocation of development assurance levels: An extension to HiP-HOPS

Controlling the allocation of safety requirements across a system's architecture from the early stages of development is an aspiration embodied in numerous major safety standards. Manual approaches of applying this process in practice are ineffective due to the scale and complexity of modern electronic systems. In the work presented here, we aim to address this issue by presenting an extension to the dependability analysis and optimisation tool, HiP-HOPS, which allows automatic allocation of such requirements. We focus on aerospace requirements expressed as Development Assurance Levels (DALs); however, the proposed process and algorithms can be applied to other common forms of expression of safety requirements such as Safety Integrity Levels. We illustrate application to a model of an aircraft wheel braking system

Modelling flexible manufacturing systems through discrete event simulation

As customisation and product diversification are becoming standard, industry is looking for strategies to become more adaptable in responding to customer’s needs. Flexible manufacturing systems (FMS) provide a unique capability where there is a need to provide efficiency through production flexibility. Full potential of FMS development is difficult to achieve due to the variability of components within this complex manufacturing system. It has been recognised that there is a requirement for decision support tools to address different aspects of FMS development. Discrete event simulation (DES) is the most common tool used in manufacturing sector for solving complex problems. Through systematic literature review, the need for a conceptual framework for decision support in FMS using DES has been identified. Within this thesis, the conceptual framework (CF) for decision support for FMS using DES has been proposed. The CF is designed based on decision-making areas identified for FMS development in literature and through industry stakeholder feedback: set-up, flexibility and schedule configuration. The CF has been validated through four industrial simulation case studies developed as a part of implementation of a new FMS plant in automotive sector. The research focuses on: (1) a method for primary data collection for simulation validated through a case study of material handling robot behaviour in FMS; (2) an approach for evaluation of optimal production set-up for industrial FMS with DES; (3) a DES based approach for testing FMS flexibility levels; (4) an approach for testing scheduling in FMS with the use of DES. The study has supported the development of systematic approach for decision making in FMS development using DES. The approach provided tools for evidence based decision making in FMS

Decision support for build-to-order supply chain management through multiobjective optimization

This paper aims to identify the gaps in decision-making support based on multiobjective optimization for build-to-order supply chain management (BTOSCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting multiobjective optimization (MOO) techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted and future research directions to better exploit the decision support capabilities of MOO are proposed

Decision support for build-to-order supply chain management through multiobjective optimization

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.This paper aims to identify the gaps in decision-making support based on multiobjective optimization (MOO) for build-to-order supply chain management (BTO-SCM). To this end, it reviews the literature available on modelling build-to-order supply chains (BTO-SC) with the focus on adopting MOO techniques as a decision support tool. The literature has been classified based on the nature of the decisions in different part of the supply chain, and the key decision areas across a typical BTO-SC are discussed in detail. Available software packages suitable for supporting decision making in BTO supply chains are also identified and their related solutions are outlined. The gap between the modelling and optimization techniques developed in the literature and the decision support needed in practice are highlighted. Future research directions to better exploit the decision support capabilities of MOO are proposed. These include: reformulation of the extant optimization models with a MOO perspective, development of decision supports for interfaces not involving manufacturers, development of scenarios around service-based objectives, development of efficient solution tools, considering the interests of each supply chain party as a separate objective to account for fair treatment of their requirements, and applying the existing methodologies on real-life data sets.Brunel Research Initiative and Enterprise Fund (BRIEF

A Benes Based NoC Switching Architecture for Mixed Criticality Embedded Systems

Multi-core, Mixed Criticality Embedded (MCE) real-time systems require high timing precision and predictability to guarantee there will be no interference between tasks. These guarantees are necessary in application areas such as avionics and automotive, where task interference or missed deadlines could be catastrophic, and safety requirements are strict. In modern multi-core systems, the interconnect becomes a potential point of uncertainty, introducing major challenges in proving behaviour is always within specified constraints, limiting the means of growing system performance to add more tasks, or provide more computational resources to existing tasks. We present MCENoC, a Network-on-Chip (NoC) switching architecture that provides innovations to overcome this with predictable, formally verifiable timing behaviour that is consistent across the whole NoC. We show how the fundamental properties of Benes networks benefit MCE applications and meet our architecture requirements. Using SystemVerilog Assertions (SVA), formal properties are defined that aid the refinement of the specification of the design as well as enabling the implementation to be exhaustively formally verified. We demonstrate the performance of the design in terms of size, throughput and predictability, and discuss the application level considerations needed to exploit this architecture

Generation of model-based safety arguments from automatically allocated safety integrity levels

To certify safety-critical systems, assurance arguments linking evidence of safety to appropriate requirements must be constructed. However, modern safety-critical systems feature increasing complexity and integration, which render manual approaches impractical to apply. This thesis addresses this problem by introducing a model-based method, with an exemplary application based on the aerospace domain.Previous work has partially addressed this problem for slightly different applications, including verification-based, COTS, product-line and process-based assurance. Each of the approaches is applicable to a specialised case and does not deliver a solution applicable to a generic system in a top-down process. This thesis argues that such a solution is feasible and can be achieved based on the automatic allocation of safety requirements onto a system’s architecture. This automatic allocation is a recent development which combines model-based safety analysis and optimisation techniques. The proposed approach emphasises the use of model-based safety analysis, such as HiP-HOPS, to maximise the benefits towards the system development lifecycle.The thesis investigates the background and earlier work regarding construction of safety arguments, safety requirements allocation and optimisation. A method for addressing the problem of optimal safety requirements allocation is first introduced, using the Tabu Search optimisation metaheuristic. The method delivers satisfactory results that are further exploited for construction of safety arguments. Using the produced requirements allocation, an instantiation algorithm is applied onto a generic safety argument pattern, which is compliant with standards, to automatically construct an argument establishing a claim that a system’s safety requirements have been met. This argument is hierarchically decomposed and shows how system and subsystem safety requirements are satisfied by architectures and analyses at low levels of decomposition. Evaluation on two abstract case studies demonstrates the feasibility and scalability of the method and indicates good performance of the algorithms proposed. Limitations and potential areas of further investigation are identified