CARING: Computational Analytics for Resilient and Intelligent Networks Group

Learning and Decision Intelligence

Background

Large-scale networks provide a natural representation of complex systems comprising thousands of interacting components, but their scale and structural complexity create significant challenges for optimization and decision-making. Many network optimization problems are formulated as mixed-integer programming (MIP) models, which are often NP-hard and become increasingly difficult to solve as the problem size grows. At the same time, high-dimensional data create challenges for developing predictive and decision-support models that are both accurate and interpretable. Learning-augmented optimization offers an opportunity to leverage information generated during the search process to improve decision-making, while causal inference provides complementary tools for uncovering relationships and improving the interpretability of data-driven models. However, effectively integrating learning with optimization and developing robust approaches that generalize across complex networked systems remain important challenges.

Objective

This research seeks to develop scalable learning-augmented optimization and safety-driven policies for deterministic and stochastic formulations of MIP models and multi-agent sequential decision-making on large-scale networks. We use benchmark problems and multiple city-scale networks to demonstrate the added value of our approaches relative to baseline optimization and reinforcement learning algorithms in terms of solution quality, convergence rate, and computational time.

Publications

  1. Mohebbi, S., Pamukcu, E., Bozdogan, H., (2019). “A new data adaptive elastic net predictive model using hybridized smoothed covariance estimators with information complexity”, Journal of Statistical Computation and Simulation, 89(6), 1060-1089.
  2. Aslani, B., Mohebbi, S., (2023). “Ensemble framework for causality learning with heterogeneous Directed Acyclic Graphs through the lens of optimization”, Computers and Operations Research, 152, 106148.
  3. Aslani, B., Mohebbi, S., Ougthon. E. (2024). “A systematic review of optimization methods for recovery planning in cyber-physical infrastructure networks: current state and future trends”, Computers & Industrial Engineering, 192, 110224. https://doi.org/10.1016/j.cie.2024.110224
  4. Aslani, B., Mohebbi, S., (2024). “Learn to decompose multi-objective optimization models for large-scale networks”, International Transactions in Operational Research, 31(2), 949-978. https://doi.org/10.1111/itor.13169
  5. Aslani, B., Mohebbi, S., (2025). “A learning-augmented branch-and-price for large-scale integrated network design and scheduling problem in road restoration” [Under Review; 2024 Best Track Paper Award of the IISE Operations Research Division].
  6. Aslani, B., Mohebbi, S., Ji, R., (2025). “Learn-to-construct cuts in nested benders decomposition with application to large-scale stochastic multi-stage network design and scheduling” [Under Review]. Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5895134
  7. Murali, P.S., Mohebbi, S., (2026). “Safety-driven decentralized decisions in networks: An indicator-based reinforcement learning approach”, IISE Transactions, 1–13, https://doi.org/10.1080/24725854.2026.2646938

Funding Source

Mission-Focused Applied Prototyping, Air Force Research Laboratory, AFCENT (2023-2026), Co-PI, $7,442,840.

Center for Resilient and Sustainable Communities, George Mason University (2021-2023), PI, $31,640.

Postdoctoral Scholar and GRAs

Dr. Babak Aslani (Postdoc), Pavithra Sripathanallur Murali (PhD Student), Andrew Moseman (MSc student)

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