Operations Research
Uncertain Linear Programs: Extended Affinely Adjustable Robust Counterparts
Operations Research
Robust Approximation to Multiperiod Inventory Management
Operations Research
Optimality of Affine Policies in Multistage Robust Optimization
Mathematics of 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
IEEE/ACM Transactions on Networking (TON)
Optimization Under Probabilistic Envelope Constraints
Operations Research
Robust local search for solving RCPSP/max with durational uncertainty
Journal of Artificial Intelligence Research
Robust Storage Assignment in Unit-Load Warehouses
Management Science
Multiple Objectives Satisficing Under Uncertainty
Operations Research
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Stochastic optimization, especially multistage models, is well known to be computationally excruciating. Moreover, such models require exact specifications of the probability distributions of the underlying uncertainties, which are often unavailable. In this paper, we propose tractable methods of addressing a general class of multistage stochastic optimization problems, which assume only limited information of the distributions of the underlying uncertainties, such as known mean, support, and covariance. One basic idea of our methods is to approximate the recourse decisions via decision rules. We first examine linear decision rules in detail and show that even for problems with complete recourse, linear decision rules can be inadequate and even lead to infeasible instances. Hence, we propose several new decision rules that improve upon linear decision rules, while keeping the approximate models computationally tractable. Specifically, our approximate models are in the forms of the so-called second-order cone (SOC) programs, which could be solved efficiently both in theory and in practice. We also present computational evidence indicating that our approach is a viable alternative, and possibly advantageous, to existing stochastic optimization solution techniques in solving a two-stage stochastic optimization problem with complete recourse.