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

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

Multi-Objective Evolutionary Neural Network to Predict Graduation Success at the United States Military Academy

This paper presents an evolutionary neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A pareto-based, multi-objective evolutionary algorithm utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) fitness evaluation scheme simultaneously evolves connection weights and identifies the neural network topology using network complexity and classification accuracy as objective functions. A combined vector-matrix representation scheme and differential evolution recombination operators are employed. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. The inputs to the evolutionary neural network model are used to classify students as: graduates, late graduates, or non-graduates. Results of the hybrid method show higher mean classification rates (88%) than the current methodology (80%) with a potential savings of $130M. Additionally, the proposed method is more efficient in that a less complex neural network topology is identified by the algorithm

Application of an Artificial Neural Network to Predict Graduation Success at the United States Military Academy

This paper presents a neural network approach to classify student graduation status based upon selected academic, demographic, and other indicators. A multi-layer feedforward network with backpropagation learning is used as the model framework. The model is trained, tested, and validated using 5100 student samples with data compiled from admissions records and institutional research databases. Nine input variables consist of categorical and numeric data elements including: high school rank, high school quality, standardized test scores, high school faculty assessments, extra-curricular activity score, parent\u27s education status, and time since high school graduation. These inputs and the multi-layer neural network model are used to classify students as: graduates, late graduates, or non-graduates. Several neural network architectures are examined and compared by run time, minimum mean square error achieved (MSE), mean correct classification rate, precision, recall, and specificity. A multi-layer neural network with 50 hidden neurons, momentum value of 0.8, and learning rate of 0.1, with hyperbolic tangent hidden neuron activation functions was able to accurately predict graduation success and achieved the best performance with classification accuracy exceeding 95%, high recall, high precision, and high specificity. This prediction model may be used to inform admission decisions and identify opportunities for required remediation with the potential to improve graduation rates, increase student retention, reduce late graduation, and reduce first-term course failures

Modeling and Analysis of the Rotor Blade Refurbishment Process at the Corpus Christi Army Depot

Much of the Army’s equipment is coming to the end of its planned life cycle.  At the same time, the Department of Defense and the Army are facing severe budget reductions for the foreseeable future.  As a result, the planned modernization and acquisition of new equipment will be delayed.  The Army is now forced to keep and maintain current equipment as opposed to retiring old systems and buying new ones.  With the increased investment in the current systems, the organizations and depots that maintain and refurbish the Army’s equipment are becoming increasingly valuable assets.  Corpus Christi Army Depot (CCAD) is the Army’s only facility for repair and overhaul of rotary wing aircraft.  CCAD receives approximately 10 rotor blades per day for the Black Hawk helicopter.  Each blade is routed through a detailed inspection and rework process consisting of approximately 67 sequential operations which take approximately 45 days per blade.  Recently CCAD has expanded and reorganized the rotor blade refurbishment facility which provides an opportunity to re-examine processes, adjust positioning of work stations, and improve efficiency.  In this research we develop a discrete-event simulation model of the CCAD rotor blade refurbishment process in order to identify inefficiencies and examine “what if” scenarios to improve key performance metrics.  The key performance metrics used to analyze model input include throughput, work in progress, mean queue time, mean queue size, and workstation utilization.  The baseline model revealed that there were two crucial bottlenecks that severely limited the throughput and overall performance of the refurbishment process.  Adjusting the capacities of these workstations was very effective in reducing the number of blades in WIP and reducing the impact of the queues in front of these stations, but failed to increase the throughput to the desired amount.  Additionally, we found that the loss of one whirl tower’s production would not be a significant factor for CCAD’s performance in terms of throughput since operating with only one whirl tower did not significantly impact metrics of interest for the process

Improving the Efficiency of Military Vehicle Outload and Deployment

The United States military must maintain the ability to rapidly deploy, world-wide, under severe time constraints. As a result, units and organizations have developed standardized, documented processes and procedures to quickly deploy personnel, equipment, and supplies. This research examines a typical military vehicle outload process, models the process with a discrete-event simulation, and identifies opportunities to increase process efficiency. The recommended improvements are incorporated within the simulation to identify the impacts of the changes. Model analysis reveals that an increase in a critical resource (i.e. vehicle inspection teams) can significantly reduce the time required to process a 350-vehicle fleet. Additionally, automating the hazardous material (HAZMAT) documentation and vehicle weight and center of balance computations resulted in time savings, although less significant. It is possible to implement these two automated activities across all installations, further improving deployment operations. With only minor modifications, the presented model can be adjusted to replicate other installation deployment processes and can have significant impacts on how the U.S. Army and U.S. Air Force deploy equipment

A Fuzzy Genetic Algorithm Approach to Generate and Assess Meta-Architectures for Non-Line of Site Fires Battlefield Capability

This paper presents a fuzzy genetic algorithm approach to generate, assess, and select a System of Systems (SoS) meta-architecture through coupled executable models. A type-1 fuzzy assessor is used to transform crisp performance attribute inputs into a meta-architecture assessment for use as part of the fitness function of a genetic algorithm. This algorithm is applied to the generation, assessment, and selection of a meta-architecture for a hypothetical lethal, non-line of sight fires SoS for which the key performance attributes are affordability, flexibility, performance, robustness, and reliability. Combinations of existing systems that have nonlinear interactions are assessed and compared to the United States Military Future Combat System. Results show that this approach produces architectures that provide the same performance without requiring the purchase of any new systems, potentially saving billions of dollars