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Showing 1 to 5 of 5 for “"Multistage Stochastic Programming"”.
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Data-driven robust solution schemes for sequential decision making
… alternative to sample average approximation for multistage stochastic programming with Markovian uncertainty and introduces robust and distributionally robust optimization frameworks for two additional problem domains: fairness-aware stochastic optimal control and system identification from a …
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The Markov chain Monte Carlo approach to importance sampling in stochastic programming
Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this …
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Multi-stage Stochastic Programming Models in Production Planning
… we study a series of closely related multi-stage stochastic programming models in production planning, from both a modeling and an algorithmic point of view. We first consider a very simple multi-stage stochastic lot-sizing problem, involving a single item with no fixed charge and capacity …
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A stochastic programming framework for financial intermediaries liquidity in South Africa
… the portfolio optimisation. We propose a novel multistage stochastic programming methodology for liquid asset control. Thus we define how to construct and solve stochastic programming models for liquidity needs-driven sub-portfolios. Our approach is based on scenario trees and makes no …
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Stochastic design optimization of modular, reconfigurable, persistent support platforms in Earth orbit
DSpace SAF Submission Ingestion Package generated from Vireo submission #13750 on 2019-08-22 at 14:44:50