Stochastic Modeling & Simulation

The Ohio State University hosts an exciting research program on stochastic modeling, stochastic optimization, and simulation. Much of the research is on modeling, analysis, and optimization of real-world systems involving uncertainty.  ISE faculty focus on a variety of emerging applications including cloud computing, cyber security, energy systems, healthcare analytics, manufacturing, supply chain management, transportation networks, voting systems, and water resource management. The faculty aims at developing new methods that integrate data, modeling, algorithms and application into innovative solutions for efficient management of real-world systems under uncertainty.  

Scope

Increasingly, decision-makers want “data-driven” recommendations. Going from data to models to decisions inevitably involves statistics and uncertainty, i.e., “stochastic” modeling.  Examples of our projects include simulation-based support in manufacturing, provisioning and pricing of cloud computing resources, optimizing water system holdings for a region, and supporting decisions about which computers need to be cleaned of vulnerabilities in a network. Research in this area concerns developing suitable stochastic models, simulation-based optimization methods, analytical or approximation methods for predicting performance, helping make strategic decisions on resources, and identifying the optimal design for such systems.

Approach

We capture the uncertainty using probabilistic models and use probability theory, statistics, and simulation to predict behavior or performance. We often embed these within optimization models and methods to make decisions under uncertainty.  Research in stochastic modeling often focuses on developing analytical tools for complex models. For example, many real-life systems consisting of customers that wait for service from a collection of servers, can be represented as queueing models. Queuing theory is a body of models and analytical techniques for predicting performance of different designs for such systems. In practice, approximations, numerical methods, or computer simulation are employed often.     

Methodologies

Stochastic processes
Queueing theory
Markov decision theory
Stochastic optimization
Simulation
Data analytics
Machine learning
Game theory

 

Applications

Cloud computing
Cyber security
Energy systems
Manufacturing
Supply chain management
Transportation networks
Voting systems
Water resources management

 

Concentration Faculty


Stochastic Modeling: 

Güzin Bayraksan   |   Parinaz Naghizadeh   |   Cathy H. Xia


Data Analysis and Statistics: 

Theodore Allen