UC IEOR combines stochastic modeling, optimization, and data science. Students build digital twins, simulate queuing networks, and develop decision-support tools for real clients.
Mixed-integer program routing electric delivery vans under range constraints
Queueing-theory simulation reducing patient wait times in a children’s hospital
Reinforcement-learning policy for dynamic inventory pricing in e-commerce
Risk-parity portfolio optimizer using convex optimization and Python CVXPY
Policy memo on fair algorithmic scheduling for gig-platform workers
Digital twin of a semiconductor fab minimizing throughput bottlenecks
Crowdsourced bike-share rebalancing algorithm tested in real time
Stochastic knapsack model prioritizing wildfire resource dispatch
VR ergonomics assessment reducing musculoskeletal strain on warehouse pickers
Machine-learning demand forecast feeding robust supply-planning model
Linear-fractional assignment optimizing organ-donor matching equity
Ethnographic study of control-room decision making in microgrid operations
Monte-Carlo scenario analysis of shipping-route disruptions in the Red Sea
Interactive dashboard visualizing carbon footprints across supply-chain tiers
Text-mining maintenance logs predicting aircraft part failures
Workshop teaching middle-schoolers optimization via LEGO factory games
Network analysis of financial contagion risk between global banks
Critical essay on algorithmic bias in loan-approval scoring systems
Augmented-reality interface guiding assembly-line workers through variant builds
Simulation game evaluating policy trade-offs in pandemic vaccine rollout
Streamline systems and power smart decisions with UC’s IEOR program.
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