Reduction of nonanticipativity constraints in multistage stochastic programming problems with endogenous and exogenous uncertainty.

*(English)*Zbl 1402.90105F. Hooshmand and S. A. MirHassani [Optim. Methods Softw. 31, No. 2, 359–376 (2016; Zbl 1382.90069)] proposed a polynomial time algorithm which is able to identify all redundant nonanticipativity constraints (NACs) in a stochastic programming (SP) problem with only endogeneous uncertainty.

In this paper, they extend this algorithm and present a new method which is able to make the upper most possible reduction in the number of NACs in any SP with both exogenous and endogenous uncertain parameters. The performance of the proposed approach is evaluated on ten randomly generated instances of simple SP problems.

In this paper, they extend this algorithm and present a new method which is able to make the upper most possible reduction in the number of NACs in any SP with both exogenous and endogenous uncertain parameters. The performance of the proposed approach is evaluated on ten randomly generated instances of simple SP problems.

Reviewer: I. M. Stancu-Minasian (Bucureşti)

##### MSC:

90C15 | Stochastic programming |

##### Keywords:

multistage stochastic programming; endogenous uncertainties; exogenous uncertainties; redundant nonanticipativity constraints; constraint reduction##### Software:

AIMMS
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\textit{F. Hooshmand} and \textit{S. A. MirHassani}, Math. Methods Oper. Res. 87, No. 1, 1--18 (2018; Zbl 1402.90105)

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