Advanced Methods

Mission Statement

The ASDL Advanced Methods Division aims to educate highly qualified professionals and to advance the state-of-the-art of systems engineering and related methodologies to address current and future design challenges.
AMRLdiagram.PNG

Overview

The Advanced Methods Division aims to provide methods development support across all ASDL divisions. This division establishes a collaborative environment to share and develop new ideas advancing the state-of-the art in systems engineering methods. Further, we are constantly striving to transition new methods to the classroom in order to train the next egeneration workforce.

Contacts

Dr. Elena Garcia,
Division Chief

Dr. Burak Bagdatli
Simulation and Statistics

Dr. Selcuk Cimtalay
Model Based Systems Engineering

Dr. Adam Cox
Production Analytics

Dr. Alexia Payan
Large Scale Optimization

Dr. Russell Peak
Model Based Systems Engineering Lead

Dr. Miguel Walter
Uncertainty Quantification and Management

Dr. Miyako Wilson
Model Based systems Engineering

Areas of Research

  • Large Scale Optimization

Large scale optimization deals with problem spaces in which millions of variables may be necessary to solve a highly constrained problem. Some example applications of large scale optimization are: Supply-chain logistics, transportation networks or airline scheduling.

  • Model Based Systems Engineering

Model Based Systems Engineering brings the capabilities provided by modeling to bear on the systems engineering process moving from a document centric approach to one which employs modeling through the system hierarchy to support design decisions and requirements verification. It brings together all the different stakeholder views under a common core of structured, integrated models providing a holistic view and improving communications.

  • Production Analytics

Understanding the myriad influences that impact a successful design is a core goal of the ASDL. Manufacturing INfluenced Design (MInD) and techniques to enable the factory of the future are a key component of this goal.

  • Uncertainty Quantification and Management

As opposed to deterministic design, probabilistic design assumes that inputs and responses are uncertain and can be represented with distributions. This uncertainty due to noise, external factors or lack of knowledge about the design imust be quantified, propagated and managed.

  • Decision Science

Human decision making addresses the biases, prejudices and preferences of humans when making decisions. From an engineering perspective, it seeks to quantify and predict how humans will respond in concept selection activities or design amidst various external conditions or stimuli.

  • Strategic Planning

Identifying and assessing the impact of new technologies is an essential step before investment in any portfolio of technologies. Various methods have been developed and are currently researched to improve technology forecasting and selection to aid strategic decision makers.