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The definition of reliability may not be readily applicable for repairable systems. Our recent work has shown that multiple metrics are needed to fully account for the performance of a repairable system under uncertainty. Optimal tradeoffs among a minimal set of metrics can be used in the design and maintenance of these systems. A minimal set of metrics provides the most information about the system with the smallest number of metrics using a set of desirable properties. Critical installations such as a remote microgrid powering a military installation require a careful consideration of cost and repair strategies. This is because of logistical challenges in performing repairs and supplying necessary spare parts, particularly in unsafe locations. This paper shows how a minimal set of metrics enhances decision making in such a scenario. It enables optimal tradeoffs between critical attributes in decision making, while guaranteeing that all important performance measures are satisfied. As a result, cost targets and inventory planning can be achieved in an optimal way. We demonstrate the value of the proposed approach using a US Army smart-charging microgrid installation.

Tradespace analysis and exploration is used to frame a design problem. By taking stock of available technologies, predictions of the performance of a system defined from a combinatorial combination of technologies (from say a morphological matrix) can be made. Based on these assessments, tradeoffs between functional performance objectives (often termed simply Functional Objectives or FOs) can be made. The result of these performance tradeoffs or Trades, can then be used to define a target design space for a problem. That design space can then be characterized with criteria to determine the viability of the tradespace and the design problem. However, the cost to develop the morphological matrix for the tradespace can be prohibitive. The tradespace at the US Army DEVCOM Ground Vehicle Systems Center (GVSC) took more than 2 years of effort by multiple staff and technical experts to develop and allows for the consideration of more than 1021 vehicles. To develop enhanced approaches to tradespace analysis and exploration to enhance programmatic decision-making, a simulated tradespace based on “synthetic data” is necessary. For tradespace studies within the Clemson University Virtual Prototyping of Ground Systems (VIPR-GS) it was necessary to develop a synthetic tradespace model to serve as a basis for evaluating improved approaches to tradespace analysis, exploration and decision-making methods. Within this work, we describe the state-of-the-art for developing models of the tradespace, formulations of functional objectives and defined models to represent different synthetic variable types to produce a synthetic tradespace with far less effort. Using this approach, we demonstrate the development of an example of a synthetic tradespace for small semi-autonomous ground vehicles developed within the VIPR Center that can be used to evaluate vehicle designs for the Clemson Deep Orange Project Vehicle and at GVSC. Finally, we will explore how this tradespace model can be used to facilitate decision-making surrounding the tradespace in the future.