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Dynamic multistage stochastic optimization programs§offer a possibility to include uncertainty into§optimization models, providing a contemporary set of§tools for modern management sciences with wide range§of applications.§In order to solve realistic real-world stochastic§optimization programs, the approximation of the§underlying stochastic process describing the future§uncertainty is performed. In this work, a tree-based§discretization technique utilizing conditional§transportation distance is considered, as it is well§suited for the approximation of multi-stage§stochastic programming problems. Corresponding§convergence properties are investigated. The relation§between§the approximation quality of the probability model§and the quality of the solution is established.§An example of application, multistage inventory§control, is used to verify theoretical results. The§numerical solution and the obtained error bounds are§calculated explicitly.
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