6 papers
Computing Rare Probabilities of Voltage Collapse
Tongtong Jin, Anirudh Subramanyam, D. Adrian Maldonado
This paper introduces a framework based on Large Deviation Theory (LDT) to accurately and efficiently compute the rare probabilities of voltage collapse. We formulate the problem a…
Decision-Scaled Scenario Approach for Rare Chance-Constrained Optimization
Jaeseok Choi, Anand Deo, Constantino Lagoa +1
Chance-constrained optimization is a suitable modeling framework for safety-critical applications where violating constraints is nearly unacceptable. The scenario approach is a pop…
Machine Learning-Enabled Large-Scale Capacity Expansion Planning under Uncertainty
Taehyeon Kwon, Anirudh Subramanyam
Capacity expansion planning under uncertainty requires selecting a scenario count and representative operational horizon to estimate average production costs. Small choices risk un…
Neural Embedded Mixed-Integer Optimization for Location-Routing Problems
Waquar Kaleem, Doyoung Lee, Changhyun Kwon +1
We present a framework that combines machine learning with mixed-integer optimization to solve the Capacitated Location-Routing Problem (CLRP), a classical NP-hard problem that int…
Extreme-Scale EV Charging Infrastructure Planning for Last-Mile Delivery Using High-Performance Parallel Computing
Waquar Kaleem, Taner Cokyasar, Jeffrey Larson +3
This paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide whe…
Correction to: A Lagrangian dual method for two-stage robust optimization with binary uncertainties
Henri Lefebvre, Anirudh Subramanyam
We provide a correction to the sufficient conditions under which closed-form expressions for the optimal Lagrange multiplier are provided in arXiv:2112.13138 [math.OC]. We first pr…