Towards a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process

Systems engineering processes coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. To this end, the objective of this paper is to develop and analyze a principal-agent model of a one-shot (single iteration), shallow (one level of hierarchy) systems engineering process. We assume that the systems engineer maximizes the expected utility of the system, while the subsystem engineers seek to maximize their expected utilities. Furthermore, the systems engineer is unable to monitor the effort of the subsystem engineer and may not have a complete information about their types or the complexity of the design task. However, the systems engineer can incentivize the subsystem engineers by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bi-level optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities

Parallel computation using MEMS oscillator-based computing system

In recent years, parallel computing systems such as artificial neural networks (ANNs) have been of great interest. In these systems which emulate the behavior of human brains, the processing is carried out simultaneously. However, it is still a challenging engineering problem to design highly efficient hardware for parallel computing systems. We will study the properties of networks of Microelectromechanical System (MEMS) oscillators to explore their capabilities as parallel computing infrastructure. Furthermore, we simulate the time-variant states of MEMS oscillators network under various initial conditions and performance of certain tasks. Recent theoretical results show that networks of MEMS oscillators have some properties such as phase locking, frequency locking, and synchronization which make the parallel computation possible. We demonstrate how networks of MEMS oscillators can be used for parallel computing in pattern recognition tasks through a series of Jupyter notebooks. The simulations results show that MEMS oscillator networks are able to memorize and recognize multiple patterns as well as perform image convolution with a structure consisting of a multitude of 2-oscillator networks. Hence, MEMS oscillator network is a potential candidate for future embedded computing system to improve computational performance of problems

Optimization under Uncertainty Tool for Modeling Porous Lithium-Ion Batteries

The motivation of this tool is to optimize the performance of battery based on energy output. During the manufacturing process, several parameters such as cathode thickness, the volume concentration of cathode and radius of negative active materials are subject to uncertainty. To optimize battery performance, it is significant to quantify those uncertainties through electrochemical multiscale computer simulation. Hence, this tool will focus on the optimization of the performance of lithium-ion battery under different currents. This tool will consist of a module on visualized generator of uncertainty input, an electrochemical system simulator, a visualization of output optimization module. First, the uncertainty input generator provides the option for selecting one of several statistical models for the input parameter distributions. The method of moment matching and Gauss-Hermite quadrature formula are used to simulate distribution. Simulations are performed using an existing electrochemical system simulator that in turn uses the data obtained from the uncertainty input generator to simulate energy and power, which can be considered as a black-box function. The simulation results are quantified graphically through error bar plots that visualize the impact of the uncertainties. For the optimization part, the variation and optimization of power and energy densities as a function of current density of the battery electrode are presented using GPy package and the result are obtained and plotted under uncertain input parameters. Bayesian optimization will be utilized to determine the global optimization through the black-box function. Additional work may be needed to include more of the uncertain variables in this framework

Game-Theoretic Modeling of Multi-Agent Systems: Applications in Systems Engineering and Acquisition Processes

The process of acquiring the large-scale complex systems is usually characterized with cost and schedule overruns. To investigate the causes of this problem, we may view the acquisition of a complex system in several different time scales. At finer time scales, one may study different stages of the acquisition process from the intricate details of the entire systems engineering process to communication between design teams to how individual designers solve problems. At the largest time scale one may consider the acquisition process as series of actions which are, request for bids, bidding and auctioning, contracting, and finally building and deploying the system, without resolving the fine details that occur within each step. In this work, we study the acquisition processes in multiple scales. First, we develop a game-theoretic model for engineering of the systems in the building and deploying stage. We model the interactions among the systems and subsystem engineers as a principal-agent problem. We develop a one-shot shallow systems engineering process and obtain the optimum transfer functions that best incentivize the subsystem engineers to maximize the expected system-level utility. The core of the principal-agent model is the quality function which maps the effort of the agent to the performance (quality) of the system. Therefore, we build the stochastic quality function by modeling the design process as a sequential decision-making problem. Second, we develop and evaluate a model of the acquisition process that accounts for the strategic behavior of different parties. We cast our model in terms of government-funded projects and assume the following steps. First, the government publishes a request for bids. Then, private firms offer their proposals in a bidding process and the winner bidder enters in a contract with the government. The contract describes the system requirements and the corresponding monetary transfers for meeting them. The winner firm devotes effort to deliver a system that fulfills the requirements. This can be assumed as a game that the government plays with the bidder firms. We study how different parameters in the acquisition procedure affect the bidders’ behaviors and therefore, the utility of the government. Using reinforcement learning, we seek to learn the optimal policies of involved actors in this game. In particular, we study how the requirements, contract types such as cost-plus and incentive-based contracts, number of bidders, problem complexity, etc., affect the acquisition procedure. Furthermore, we study the bidding strategy of the private firms and how the contract types affect their strategic behavior