On the Power of Robust Solutions in Two-Stage Stochastic and Adaptive Optimization Problems
Mathematics of Operations Research
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
IEEE Transactions on Robotics
Mathematics of Operations Research
Strong Duality in Robust Convex Programming: Complete Characterizations
SIAM Journal on Optimization
Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance
Journal of Intelligent and Robotic Systems
Capacity Planning in the Semiconductor Industry: Dual-Mode Procurement with Options
Manufacturing & Service Operations Management
Technical Note---A Sampling-Based Approach to Appointment Scheduling
Operations Research
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In this paper we discuss computational complexity and risk averse approaches to two and multistage stochastic programming problems. We argue that two stage (say linear) stochastic programming problems can be solved with a reasonable accuracy by Monte Carlo sampling techniques while there are indications that complexity of multistage programs grows fast with increase of the number of stages. We discuss an extension of coherent risk measures to a multistage setting and, in particular, dynamic programming equations for such problems.