Multi-stage stochastic optimization applied to energy planning
Mathematical Programming: Series A and B
Cut sharing for multistage stochastic linear programs with interstage dependency
Mathematical Programming: Series A and B
Optimization of Convex Risk Functions
Mathematics of Operations Research
Mathematics of Operations Research
On the convergence of stochastic dual dynamic programming and related methods
Operations Research Letters
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We consider interstage dependent stochastic linear programs where both the random right-hand side and the model of the underlying stochastic process have a special structure. Namely, for equality constraints (resp. inequality constraints) the right-hand side is an affine function (resp. a given function bt) of the process value for the current time step t. As for m-th component of the process at time step t, it depends on previous values of the process through a function htm.For this type of problem, to obtain an approximate policy under some assumptions for functions bt and htm, we detail a stochastic dual dynamic programming algorithm. Our analysis includes some enhancements of this algorithm such as the definition of a state vector of minimal size, the computation of feasibility cuts without the assumption of relatively complete recourse, as well as efficient formulas for sharing optimality and feasibility cuts between nodes of the same stage. The algorithm is given for both a non-risk-averse and a risk-averse model. We finally provide preliminary results comparing the performances of the recourse functions corresponding to these two models for a real-life application.