Team: Dr. Negar Soheili, Lisa Bonnett
Overview
This project builds a decision intelligence framework for designing infrastructure that adapts to changing climate conditions. Traditional infrastructure design uses static assumptions about climate - our framework incorporates dynamic climate projections and uncertainty quantification to produce designs that remain effective across a range of future scenarios.
Approach
We formulate multi-stage stochastic optimization models that incorporate climate projection ensembles from leading Earth system models. Self-adapting approximation algorithms allow us to solve these massive optimization problems efficiently, while real options analysis quantifies the value of building in adaptive capacity (e.g., designing a bridge that can be cost-effectively raised if sea levels exceed initial projections).
Impact
The framework has been applied to coastal flood protection design, demonstrating that adaptive strategies can reduce lifecycle costs by 20-35% compared to robust design approaches that over-build for worst-case scenarios. We are collaborating with civil engineering firms to integrate the framework into standard design workflows.
Related Publications
Self-guided approximate linear programs: randomized multi-shot approximation of discounted Markov decision processes
P. Pakiman, S. Nadarajah, N. Soheili, and Q. Lin
Management Science
Self-adapting robustness in demand learning
B. Chen, P. Pakiman, S. Nadarajah, and S. Jasin
Manufacturing & Service Operations Management
Delayed allocation in marginalized flow models for weakly coupled Markov decision processes
S. Nadarajah and A. Cire
Management Science
Self-adapting network relaxations for weakly coupled Markov decision processes
S. Nadarajah and A. Cire
Management Science
Back to the future: Revisiting a pioneering approximation of average cost Markov decision processes using a multi-shot perspective
P. Pakiman and S. Nadarajah
Operations Research
Revisiting approximate linear programming: constraint violation learning with applications to inventory control and energy storage
Q. Lin, S. Nadarajah, and N. Soheili
Management Science