CARING: Connected, Automated, Resilient, and Intelligent Networks Group

Learning to Optimize for Networks

Background

Large-scale networks can adequately capture the structure of complex systems containing thousands of nodes (e.g., cyber-physical-social components) and edges (e.g., mutual interactions). Therefore, network optimization methods have become a popular approach for addressing combinatorial optimization problems in various application domains. The majority of problems are formulated as Mixed Integer Programming (MIP) models, which become NP-hard resulting from the exponential increase in the number of decision variables and the unique structure of the feasible region for large-scale problems. To overcome the computational challenges, leveraging the valuable information during the search process to embed well-informed machine learning methods in optimization algorithms has emerged as a promising research area.

Objective

This research seeks to develop scalable learning-based optimization and safety-driven algorithms for deterministic and stochastic formulations of MIP models and sequential decision processes on large-scale networks. We use a set of benchmark problems and multiple city-scale networks to demonstrate the added value of our approach compared to baseline optimization and reinforcement learning algorithms in terms of solution quality, convergence rate, and computational time.

Publications

  1. 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
  2. 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
  3. 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].
  4. 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
  5. 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 (2023-2028), 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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