Sampling Based Approaches for Minimizing Regret in Uncertain Markov Decision Processes (MDPs) by Ahmed, Asrar, Varakantham, Pradeep, Lowalekar, Meghna, Adulyasak, Yossiri, Jaillet, Patrick

“…Given the overly conservative nature of maximin policies, recent work has proposed minimax regret as an ideal alternative to the maximin objective for robust optimization. However, existing algorithms for handling minimax regret are restricted to models with uncertainty over rewards only and they are also limited in their scalability. …”
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Learning Through the Lens of Robustness by Tsipras, Dimitris

“…Specifically, we consider the framework of robust optimization and study how these tools can be leveraged in the context of modern ML models. …”
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Machine Learning for Short-Term Load Forecasting in Smart Grids by Ibrahim, Bibi, Rabelo, Luis, Gutierrez-Franco, Edgar, Clavijo-Buritica, Nicolas

“…Therefore, energy planners use various methods and technologies to support the sustainable expansion of power systems, such as electricity demand forecasting models, stochastic optimization, robust optimization, and simulation. Electricity forecasting plays a vital role in supporting the reliable transitioning of power systems. …”
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Robust Inventory Induction under Demand Uncertainty by Robin, Arnaud

“…We formulate the problem via two-stage adaptive robust optimization with right-hand side uncertainty. First-stage variables characterize initial induction and positioning and second-stage variables capture subsequent rebalancing and order fulfillment. …”
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Integrating Optimization and Modern Machine Learning: Theory, Computation, and Healthcare Applications by Villalobos Carballo, Kimberly M.

“…In Chapter 1, we extend state-of-the-art tools from robust optimization to non-convex and non-concave settings, allowing us to generate neural networks that are robust against input perturbations. …”
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Sustainability Analytics - Lowering Emissions With Operational Efficiency by Bhakta, Shivam

“…This study introduces a robust optimization framework using integer linear programming to navigate the complex trade-offs between maintaining operational integrity and decommissioning excess physical capacity at a representative Verizon central office. …”
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Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the Ride-Hailing System by Guo, Xiaotong, Wang, Qingyi, Zhao, Jinhua

“…The proposed predictive prescription models achieve shorter customer wait times over the point-prediction-driven optimization models when future demand predictions are not so accurate, and achieve a competitive performance with respect to the cutting-edge robust optimization models.…”
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UAV mission planning under uncertainty by Sakamoto, Philemon

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Finite Adaptability in Multistage Linear Optimization by Bertsimas, Dimitris J., Caramanis, Constantine

“…Examples of such problems abound in applications of stochastic control and operations research; yet, where robust optimization has made great progress in providing a tractable formulation for a broad class of single-stage optimization problems with uncertainty, multistage problems present significant tractability challenges. …”
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A hierarchy of policies for adaptive optimization by Iancu, Dan Andrei, Parrilo, Pablo A., Bertsimas, Dimitris J

“…In this paper, we propose a new tractable framework for dealing with linear dynamical systems affected by uncertainty, applicable to multistage robust optimization and stochastic programming. We introduce a hierarchy of near-optimal polynomial disturbance-feedback control policies, and show how these can be computed by solving a single semidefinite programming problem. …”
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Robust reconnaissance asset planning under uncertainty by Culver, David M. (David Martin)

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On the approximability of adjustable robust convex optimization under uncertainty by Bertsimas, Dimitris J., Goyal, Vineet

“…In this paper, we consider adjustable robust versions of convex optimization problems with uncertain constraints and objectives and show that under fairly general assumptions, a static robust solution provides a good approximation for these adjustable robust problems. An adjustable robust optimization problem is usually intractable since it requires to compute a solution for all possible realizations of uncertain parameters, while an optimal static solution can be computed efficiently in most cases if the corresponding deterministic problem is tractable. …”
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Multifidelity approaches for optimization under uncertainty by Ng, Leo W. T., Willcox, Karen E.

“…The methods demonstrate 90% computational savings in an acoustic horn robust optimization example and practical design turnaround time in a robust wing optimization problem.…”
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Simultaneous Robust Design and Tolerancing of Compressor Blades by Dow, Eric A., Wang, Qiqi

“…These detrimental effects can be reduced by using robust optimization techniques to optimize the blade geometry. …”
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Multi-objective robust energy management for all-electric shipboard microgrid under uncertain wind and wave by Fang, Sidun, Xu, Yan

“…The problem is formulated as a bi-level robust optimization model after certain constraint decomposition. …”
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Identifying differential scheduling plans for microgrid operations under diverse uncertainties by Qiu, Haifeng, Gooi, Hoay Beng

“…To satisfy the differential operation requirements of microgrids under the nominal and uncertain scenarios, a novel three-stage close-looped robust optimization (TSCL-RO) method is proposed to obtain more practical scheduling plans. …”
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Decentralized mixed-integer optimization for robust integrated electricity and heat scheduling by Qiu, Haifeng, Vinod, Ashwin, Lu, Shuai, Gooi, Hoay Beng, Pan, Guangsheng, Zhang, Suhan, Veerasamy, Veerapandiyan

“…Firstly, a centralized two-stage robust optimization (RO) scheduling model is installed for the IEHS considering the scheduling economy under the nominal scenario and the adjustment feasibility against uncertainty. …”
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Resource provisioning under uncertainty in cloud computing by Sivadon Chaisiri

“…We also apply the robust optimization to handle the impact of the uncertainties on the optimal solution. …”
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Robust Investment-Reinsurance Optimization with Multiscale Stochastic Volatility by Pun, Chi Seng, Wong, Hoi Ying

“…We formulate the robust optimal investment and reinsurance problem for a general class of utility functions under a general SV model. …”
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Service Region Design for Urban Electric Vehicle Sharing Systems by He, L, Mak, H, Rong, Y, Shen, Z

“…To address inherent planning uncertainty with regard to adoption patterns, we employ a distributionally robust optimization framework that informs robust decisions to overcome possible ambiguity (or lacking) of data. …”