Cardoso, R. T. N.; Takahashi, R. H. C.; Cruz, F. R. B. Pareto optimal solutions for stochastic dynamic programming problems via Monte Carlo simulation. (English) Zbl 1397.90407 J. Appl. Math. 2013, Article ID 801734, 9 p. (2013). Summary: A heuristic algorithm is proposed for a class of stochastic discrete-time continuous-variable dynamic programming problems submitted to non-Gaussian disturbances. Instead of using the expected values of the objective function, the randomness nature of the decision variables is kept along the process, while Pareto fronts weighted by all quantiles of the objective function are determined. Thus, decision makers are able to choose any quantile they wish. This new idea is carried out by using Monte Carlo simulations embedded in an approximate algorithm proposed to deterministic dynamic programming problems. The new method is tested in instances of the classical inventory control problem. The results obtained attest for the efficiency and efficacy of the algorithm in solving these important stochastic optimization problems. Cited in 1 Document MSC: 90C59 Approximation methods and heuristics in mathematical programming 65C05 Monte Carlo methods 90C15 Stochastic programming PDFBibTeX XMLCite \textit{R. T. N. Cardoso} et al., J. Appl. Math. 2013, Article ID 801734, 9 p. (2013; Zbl 1397.90407) Full Text: DOI