SUBSYSTEM FAILURE ANALYSIS WITHIN THE HORIZON SIMULATION FRAMEWORK

System design is an inherently expensive and time consuming process. Engineers are constantly tasked to investigate new solutions for various programs. Model-based systems engineering (MBSE) is an up and coming successful method used to reduce the time spent during the design process. By utilizing simulations, model-based systems engineering can verify high-level system requirements quickly and at low cost early in the design process. The Horizon Simulation Framework, or HSF, provides the capability of simulating a system and verifying the system performance. This paper outlines an improvement to the Horizon Simulation Framework by providing information to the user regarding schedule failures due to subsystem failures and constraint violations. Using the C# language, constraint violation rates and subsystem failure rates are organized by magnitude and written to .csv files. Also, proper subsystem failure and constraint violation checking orders were stored for HSF to use as new evaluation sequences. The functionalities of the systemEval framework were verified by five test cases. The output information can be used for the user to improve their system and possibly reduce the total run-time of the Horizon Simulation Framework

A Generalized Reduced Linear Program for Markov Decision Processes

Markov decision processes (MDPs) with large number of states are of high practical interest. However, conventional algorithms to solve MDP are computationally infeasible in this scenario. Approximate dynamic programming (ADP) methods tackle this issue by computing approximate solutions. A widely applied ADP method is approximate linear program (ALP) which makes use of linear function approximation and offers theoretical performance guarantees. Nevertheless, the ALP is difficult to solve due to the presence of a large number of constraints and in practice, a reduced linear program (RLP) is solved instead. The RLP has a tractable number of constraints sampled from the original constraints of the ALP. Though the RLP is known to perform well in experiments, theoretical guarantees are available only for a specific RLP obtained under idealized assumptions. In this paper, we generalize the RLP to define a generalized reduced linear program (GRLP) which has a tractable number of constraints that are obtained as positive linear combinations of the original constraints of the ALP. The main contribution of this paper is the novel theoretical framework developed to obtain error bounds for any given GRLP. Central to our framework are two max-norm contraction operators. Our result theoretically justifies linear approximation of constraints. We discuss the implication of our results in the contexts of ADP and reinforcement learning. We also demonstrate via an example in the domain of controlled queues that the experiments conform to the theory