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

Scalable Learn-to-Optimize Algorithms 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 Learn-To-Optimize algorithms for deterministic and stochastic formulations of MIP models concerning 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 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., (2024). “A Learning-Augmented Branch-and-Price for Large-scale Integrated Network Design and Scheduling Problem in Road Restoration”, INFORMS Transportation Science [Under Review].

Funding Source

Air Force Research Laboratory, AFCENT (2023-2025), Co-PI, $4,650,300.

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

GRA

Pavithra Sripathanallur Murali (PhD Student), Graham Hulsey (MSc student), Christopher Wilson (Undergraduate Researcher)

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