Application of Value Focused Thinking and Fuzzy Systems to Assess System Architecture

AbstractSince a majority of resources are obligated during the design phase of a system lifecycle, critical assessment of candidate functional and system architectures is vital to identify optimal architectures before proceeding to subsequent lifecycle phases. Common challenges associated with generation and evaluation of system functional architectures include search of the expansive design space and assessment of key performance attributes that are particularly “fuzzy” and qualitative in early architecture development. Several assessment approaches have been presented in the literature to address the assessment challenge to include Quality Function Deployment (QFD), Analytical Hierarchy Process (AHP), Value-Focused Thinking (VFT), and fuzzy logic. In this research we combine the use of value functions and fuzzy assessment to assess a functional and system architecture. There are several benefits of a methodology that combines value-focused thinking and fuzzy assessment. A distinct advantage of the methodology presented is the explicit inclusion of the customer in the assessment process through validation of the TPM value functions Involving the customer in TPM value function development and validation ensures the customer has direct input regarding the TPMs and their associated value across the range of discourse The methodology presented is flexible enough to assess architectures early in the process when things are “fuzzy” as well as later when subsystem and component performance are well defined. The methodology can also be used to analyze and assess impacts of interface changes within the system architecture. . The methodology is domain independent and can be coupled with executable models linked to scenarios. The assessment methodology is applied to the architecture for a soldier knowledge acquisition system for which the key performance attributes are affordability, performance, flexibility, updateability, and availability

A Pareto Based Multi-Objective Evolutionary Algorithm Approach to Military Installation Rail Infrastructure Investment

Decision making for military railyard infrastructure is an inherently multi-objective problem, balancing cost versus capability. In this research, a Pareto-based Multi-Objective Evolutionary Algorithm is compared to a military rail inventory and decision support tool (RAILER). The problem is formulated as a multi-objective evolutionary algorithm in which the overall railyard condition is increased while decreasing cost to repair and maintain. A prioritization scheme for track maintenance is introduced that takes into account the volume of materials transported over the track and each rail segment’s primary purpose. Available repair options include repairing current 90 gauge rail, upgrade of rail segments to 115 gauge rail, and the swapping of rail removed during the upgrade. The proposed Multi-Objective Evolutionary Algorithm approach provides several advantages to the RAILER approach. The MOEA methodology allows decision makers to incorporate additional repair options beyond the current repair or do nothing options. It was found that many of the solutions identified by the evolutionary algorithm were both lower cost and provide a higher overall condition that those generated by DoD’s rail inventory and decision support system, RAILER. Additionally, the MOEA methodology generates lower cost, higher capability solutions when reduced sets of repair options are considered. The collection of non-dominated solutions provided by this technique gives decision makers increased flexibility and the ability to evaluate whether an additional cost repair solution is worth the increase in facility rail condition

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv

Design logical linguistic models to calculate necessity in trucks during agricultural cargoes logistics using fuzzy logic

The study is aimed to develop the logic-linguistic models to design a number of rules for the correct calculation of the vehicles needed, taking into account the technical, technological, and weather and climate conditions of the harvesting and transport complex. The article has shown that the construction of the design of logic-linguistic models was not performed earlier to solve the problem of the agro-industrial production transportation support, considering the opportunity of forecasting size of influences of the weather and climatic factors on improving the productivity of the harvesting and transport complex elements. It is determined that the experience of applying the fuzzy logic theory in many practice situations confirms the universality of the mathematical apparatus. This toolkit provides better results than classical approaches (set theory, probability theory). This aspect indicates the expediency of the chosen mathematical apparatus for solving the tasks. The article using fuzzy logic explores the relationship and interdependence of technical, technological factors and weather and climate conditions for modeling transport support in harvesting and transport complex. Fuzzification of the parameters is carried out, based on the compiled equations using trapezoidal and triangular membership functions. The set of rules necessary for the creation of logical-linguistic models (LLM) for each factor has been arranged. LLMs were developed for dependent parameters, which will allow further modeling of the transport support of the harvesting and transport complex in the Fuzzy Logic Toolbox application of the MATLAB package

A stochastic multi-criteria assessment of security of transportation assets

Transportation project evaluation and prioritization use traditional performance measures including travel time, safety, user costs, economic efficiency, and environmental quality. The project impacts in terms of enhancing the infrastructure resilience or mitigating the consequences of infrastructure damage in the event of disaster occurrence are rarely considered in project evaluation. This dissertation presents a methodology to address this issue so that in evaluating and prioritizing investments, infrastructure with low security can receive the attention they deserve. Secondly, the methodology can be used for evaluating and prioritizing candidate investments dedicated specifically to security enhancement. In defining security as a function of threat likelihood, asset resilience and damage consequences, this dissertation uses security-related considerations in investment prioritization thus adding further robustness in traditional evaluations. As this leads to an increase in the number of performance criteria in the evaluation, the dissertation adopts a multiple-criteria analysis approach. The methodology quantifies the overall security level for an infrastructure in terms of the threats it faces, its resilience to damage, and the consequences in the event of the infrastructure damage. The dissertation demonstrates that it is feasible to develop a security-related measure that can be used as a performance criterion in the evaluation of general transportation projects or projects dedicated specifically towards security improvement. Through a case study, the dissertation applies the methodology by measuring the risk (and hence, security) of each for bridge infrastructure in Indiana. The method was also fuzzified and a Monte Carlo simulation was run to account for unknown data and uncertainty. On the basis of the multiple types of impacts including risk impacts such as the increase in security due to each candidate investment, this dissertation shows how to prioritize security investments across the multiple infrastructure assets using multiple-criteria analysis