IEOR - Designing a More Efficient World

Research

IEOR researchers investigate the latest mathematical tools, approaches, and methodologies to make new theoretical discoveries and innovations that touch nearly every industry, making them more efficient and profitable in areas such as supply chain, logistics, manufacturing, data science, energy system, energy systems, robotics, and management.

Selected Publications

Statistical consistency of set-membership estimator for linear systems

A. Aswani and P. Hespanhol (2020), Statistical consistency of set-membership estimator for linear systems, IEEE Control Systems Letters, vol. 4, no. 3: 668-673.

X-RAY: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions

X-RAY: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions. Michael Danielczuk, Anelia Angelova, Vincent Vanhoucke, Ken Goldberg. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, October 2020. [paper], [website].

Reliable Hub Location Model for Air Transportation Networks Under Random Disruptions

H Shen, Y Liang, ZJM Shen (2019). Reliable Hub Location Model for Air Transportation Networks under Random Disruptions. To appear in Manufacturing & Service Operations Management. https://doi.org/10.1287/msom.2019.0845.

Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow

Julie Mulvaney-Kemp, Salar Fattahi, and Javad Lavaei, Smoothing Property of Load Variation Promotes Finding Global Solutions of Time-Varying Optimal Power Flow, conditionally accepted for IEEE Transactions on Control of Network Systems, 2020.

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Role of Sparsity and Structure in the Optimization Landscape of Non-convex Matrix Sensing

Igor Molybog, Somayeh Sojoudi, and Javad Lavaei, Role of Sparsity and Structure in the Optimization Landscape of Non-convex Matrix Sensing, to appear in Mathematical Programming, 2020.

Estimation and Inference for Non-Stationary Arrival Models with a Linear Trend

P. W. Glynn and Z. Zheng, “Estimation and Inference for Non-Stationary Arrival Models with a Linear Trend,” 2019 Winter Simulation Conference (WSC), National Harbor, MD, USA, 2019, pp. 3764-3773, doi: 10.1109/WSC40007.2019.9004779.