Operations Research
Constructing Risk Measures from Uncertainty Sets
Operations Research
Uncertain Linear Programs: Extended Affinely Adjustable Robust Counterparts
Operations Research
Robust Approximation to Multiperiod Inventory Management
Operations Research
A Soft Robust Model for Optimization Under Ambiguity
Operations Research
Distributionally Robust Optimization and Its Tractable Approximations
Operations Research
Robust Optimization Made Easy with ROME
Operations Research
Theory and Applications of Robust Optimization
SIAM Review
Computers and Electronics in Agriculture
A robust approach to the chance-constrained knapsack problem
Operations Research Letters
Price of Correlations in Stochastic Optimization
Operations Research
Optimization Under Probabilistic Envelope Constraints
Operations Research
Multiple Objectives Satisficing Under Uncertainty
Operations Research
Safe Approximations of Ambiguous Chance Constraints Using Historical Data
INFORMS Journal on Computing
Robust investment decisions under supply disruption in petroleum markets
Computers and Operations Research
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In this paper, we introduce an approach for constructing uncertainty sets for robust optimization using new deviation measures for random variables termed the forward and backward deviations. These deviation measures capture distributional asymmetry and lead to better approximations of chance constraints. Using a linear decision rule, we also propose a tractable approximation approach for solving a class of multistage chance-constrained stochastic linear optimization problems. An attractive feature of the framework is that we convert the original model into a second-order cone program, which is computationally tractable both in theory and in practice. We demonstrate the framework through an application of a project management problem with uncertain activity completion time.