Center for Systems Reliability
The Center for Systems Reliability (CSR) was established at Sandia National Laboratories to partner with and support U.S. government agencies and commercial organizations on technical programs that involve system and system-of-systems (SoS) reliability, readiness, sustainment and related technologies. The CSR combines Sandia’s world-class technologies, experience, software, and facilities to provide support in a broad range of areas that include reliability, availability, sustainment, logistics, operational analysis, technology management, lifecycle and total ownership cost analysis, sensitivity, uncertainty, risk analysis, industrial engineering issues, and much more.
Microgrid Design Toolkit (MDT)
Technology Refreshment Assessment Model (TRAM)
The Technology Refreshment Assessment Model (TRAM) is a genetic algorithm-based tool that improves technology management and more general decision making. TRAM has a graphical user interface that is used to construct a model of a system or problem. As with other management software, the program presents alternatives to its users, such as if and when to refresh or insert new technologies, and ways to plan for obsolescence in the event suppliers go out of business or stop making parts. TRAM has distinct advantages over the competition. At its core are genetic algorithms, which are search techniques inspired by evolutionary biology. TRAM applies its optimization analysis over the lifecycle of systems or problems giving the user defensible, time-based roadmaps for the future.
Support Enterprise Model (SEM)
The Support Enterprise Model is a discrete event simulation tool designed to model and simulate operation and support activities of a worldwide sustainment system. The general objective of SEM is to help characterize the sustainment system performance including supply, repair, and manufacturing activities over the entire life of the enterprise. To accomplish this objective the user defines and executes SEM simulations to generate statistical results characterizing the enterprise operations using different notional support and sustainment options. Results of those simulations are analyzed to make recommendations for best-case logistics system configurations that support required mission capabilities at the lowest possible cost. SEM is applicable in industries as diverse as defense, energy, aviation, and healthcare.