The Evaluation Method for Operation Efficiency of Equipment System Based on Data Farming and Mining

Through process data and results data resulted from operation efficiency simulation of equipment system, based on information measure criteria, data farming and mining method is used. Final simulation results are provided against specific models and verification is carried out on the internal relationships of equipment system and the effectiveness of equipment tactics capability. In addition, its focus is made on utilizing high-performance calculations, running it in the whole parameter space of models, obtaining a number of effective sample space data, and further analyzing it to find implicit laws and results by Big Data Mining technology and Data Visualization technology, in order to facilitate the problem-oriented system efficiency simulation

System of Systems Architecting Problems: Definitions, Formulations, and Analysis

The system of systems architecting has many applications in transportation, healthcare, and defense systems design. This study first presents a short review of system of systems definitions. We then focus on capability-based system of systems architecting. In particular, capability-based system of systems architecting problems with various settings, including system flexibility, fund allocation, operational restrictions, and system structures, are presented as Multi-Objective Nonlinear Integer Programming problems. Relevant solution methods to analyze these problems are also discussed

Facilitating the Transition to Model-Based Acquisition

Presented March 11, 2020 at IEEE Aerospace Conference 2020.© IEEEOne major benefit offered by MBSE is the ability to formalize interactions between subsystems in the design process. This formalization eases the transfer of information between parties. The process of government acquisition is likewise characterized by information transfer: diverse requirements must be altered and tracked between the requesting, responding, and evaluating parties. Thus, it is a natural extension of MBSE is to apply it to the acquisition process. This paper demonstrates a set of tools and patterns developed during a surrogate simulation of an MBSE-enabled Request for Proposal between NAVAIR and a responding contractor. In particular, the tools presented were developed from the NAVAIR Systems Model viewpoint. This paper covers four tools developed in this surrogate pilot. The first analyzes the problem of requirement generation. While standards such as the OMG SysML are being adopted by MBSE practitioners, the model literacy of all stakeholders is unlikely and may never be fully guaranteed. Document generation tools, such as OpenMBEE have been developed for SysML software, which enable presentation of descriptive information about the model. This paper demonstrates modeling patterns and a tool that translates information from native-model form into a text-based format. The second and third tools presented assist in the acquirer’s source selection process. Making use of the patterns which generate the text requirements above, Evaluation and Estimation Models are presented, which can act directly on contractors’ responses. The Evaluation Model assists the verification process by ensuring numerical requirements are satisfied. The Estimation Model compares the contractors’ claimed values with historically expected values, to assist directing the source selection experts’ focus of examination. The fourth tool presented offers a method of extracting historical traceability for model elements. This aids the acquisition process by enabling digital signoff at any stage of the acquisition process. These four tools were applied in the surrogate acquisition process for a notional UAV, and a description of this case study is presented.NAVAIR/SERC RT170/RT195, Contract HQ0034-13-D-00

Flexible and Intelligent Learning Architectures for SOS (FILA-SoS)

Multi-faceted systems of the future will entail complex logic and reasoning with many levels of reasoning in intricate arrangement. The organization of these systems involves a web of connections and demonstrates self-driven adaptability. They are designed for autonomy and may exhibit emergent behavior that can be visualized. Our quest continues to handle complexities, design and operate these systems. The challenge in Complex Adaptive Systems design is to design an organized complexity that will allow a system to achieve its goals. This report attempts to push the boundaries of research in complexity, by identifying challenges and opportunities. Complex adaptive system-of-systems (CASoS) approach is developed to handle this huge uncertainty in socio-technical systems

Multi-objective combinatorial optimization problems in transportation and defense systems

Multi-objective Optimization problems arise in many applications; hence, solving them efficiently is important for decision makers. A common procedure to solve such problems is to generate the exact set of Pareto efficient solutions. However, if the problem is combinatorial, generating the exact set of Pareto efficient solutions can be challenging. This dissertation is dedicated to Multi-objective Combinatorial Optimization problems and their applications in system of systems architecting and railroad track inspection scheduling. In particular, multi-objective system of systems architecting problems with system flexibility and performance improvement funds have been investigated. Efficient solution methods are proposed and evaluated for not only the system of systems architecting problems, but also a generic multi-objective set covering problem. Additionally, a bi-objective track inspection scheduling problem is introduced for an automated ultrasonic inspection vehicle. Exact and approximation methods are discussed for this bi-objective track inspection scheduling problem --Abstract, page iii

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