Operations Research develops rigorous mathematical and computational methods for
making better decisions in complex systems. ISEN faculty conduct research in
mathematical optimization, stochastic systems, applied probability, simulation, machine
learning, algorithms, and decision-making under uncertainty and strategic interaction.
Our research addresses problems involving resource allocation, system design,
planning, scheduling, network optimization, risk, congestion, reliability, and operational
decision-making. Faculty develop models and algorithms for continuous, discrete,
combinatorial, nonlinear, global, stochastic, robust, and large-scale optimization
problems, as well as probabilistic and simulation models for systems affected by
uncertainty, variability, and interdependence.
Applications span energy, healthcare, transportation and logistics, supply chains,
manufacturing, service systems, communication networks, autonomous systems, public
policy, and other large-scale engineered and societal systems.
Mathematical Optimization & Algorithms
Develops mathematical models, algorithms, and computational methods for
continuous, discrete, combinatorial, nonlinear, global, network, polynomial,
stochastic, robust, and large-scale optimization problems. Representative areas:
integer and combinatorial optimization; global/nonlinear optimization; network
optimization; large-scale methods; decomposition and approximation algorithms.
Stochastic Systems, Simulation & Uncertainty
Develops probabilistic, stochastic, and simulation-based models for systems
affected by uncertainty, congestion, variability, and random events.
Representative areas: queueing systems; applied probability; Markov models;
stochastic modeling; simulation; simulation optimization; ranking and selection;
risk, reliability, and sequential decision-making.
Data-Driven Operations Research & Learning
Integrates predictive and prescriptive analytics to support decisions in complex
systems using data, learning, and optimization. Representative areas: machine
learning for decision-making; reinforcement learning; statistical learning over
networks; data-driven optimization; large-scale screening and classification;
learning-augmented algorithms.
Networks, Games & Distributed Decision-Making
Studies interconnected systems in which decisions, incentives, information, and
performance are shaped by network structure and strategic interaction.
Representative areas: graph and network optimization; network analysis; game
theory; evolutionary games; multi-agent systems; distributed optimization;
distributed computing and information processing.