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Scalable Learn-to-Optimize Frameworks for Networks: Models and Applications
Scalable Learn-to-Optimize Frameworks for Networks: Models and Applications
Combinatorial Optimization Problems are a complex class of optimization problems, where the goal is to find the optimal solution concerning a given objective function from a finite candidate solution set.
Identifying the sources of practical information can inform machine learning components to guide the search and expedite the exploration effort of optimization algorithms.
Unique network features (e,g., spatial, temporal, and network measures) can be used to develop solution algorithms for networks.
This research aims to integrate machine learning and optimization algorithms to develop scalable and customizable solution methods for large-scale networks.
Publications
Publications
• Aslani, B., Mohebbi, S., & Oughton, E. (2024). A systematic review of optimization methods for recovery planning in cyber-physical infrastructure networks: Current state and future trends. Computers & Industrial Engineering, 110224. [Link]
• Aslani, B., Mohebbi, S., & Oughton, E. (2024). A systematic review of optimization methods for recovery planning in cyber-physical infrastructure networks: Current state and future trends. Computers & Industrial Engineering, 110224. [Link]
• Aslani, B., Mohebbi, S. (2024). Learn to Decompose Multi-objective Optimization Models for Large-scale Networks. International Transactions in Operational Research, 1–30. [Link]
• Aslani, B., Mohebbi, S. (2024). Learn to Decompose Multi-objective Optimization Models for Large-scale Networks. International Transactions in Operational Research, 1–30. [Link]
• Aslani, B., Mohebbi, S. (2023). Ensemble Framework for Causality Learning with Heterogeneous Directed Acyclic Graphs through the Lens of Optimization. Computers and Operations Research, 106148. [Link]
• Aslani, B., Mohebbi, S. (2023). Ensemble Framework for Causality Learning with Heterogeneous Directed Acyclic Graphs through the Lens of Optimization. Computers and Operations Research, 106148. [Link]
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