Decisions are a critical aspect of life. The success or failure of a company or even a society depends on making good decisions that may have multiple objectives. For example, governmental policy affects citizens, cities, and the earth itself. In particular, having cleaner air in metropolitan areas requires that governments decide to achieve this goal and then develop a viable plan. Such significant decisions will determine the future in far-reaching ways. Because poor decisions often lead to serious unforeseen consequences, not easily rectified, one always wishes to make good initial choices. In our global society, however, the interconnections involved in a major decision are often too complex for the human brain to grasp fully. Moreover, decision makers typically assume that the future will unfold as planned. In reality, however, decisions are made in uncertain and dynamic environments. Consequently, solutions that may have seemed optimal under ideal conditions are often far from optimal in practice, perhaps even infeasible.

Objective

The objective of the Center on Stochastic Modeling, Optimization, & Statistics (COSMOS) at The University of Texas (UT) at Arlington is to research decision-making approaches for designing, modeling, and managing complex real-world systems.

Methods

COSMOS methods seek to integrate statistics, optimization, and simulation/stochastic modeling to achieve better solutions more efficiently, and COSMOS applications customize approaches to match the needs of the decision-maker. COSMOS researchers have data science expertise in machine learning, artificial intelligence, mathematical programming, stochastic optimization, discrete-event and agent-based simulation modeling, design of experiments, statistical modeling, and statistical process monitoring.  COSMOS applications are diverse, including health care, transportation, manufacturing, energy, and environmental sustainability.