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Stochastic programming
András Prékopa
- 01 Jan 1995
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About: The article was published on 01 Jan 1995. and is currently open access. The article focuses on the topics: Stochastic programming.
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Citations
Supply chain network design under uncertainty: A comprehensive review and future research directions
TL;DR: A comprehensive review of studies in the fields of SCND and reverse logistics network design under uncertainty and existing optimization techniques for dealing with uncertainty such as recourse-based stochastic programming, risk-averse stochastics, robust optimization, and fuzzy mathematical programming are explored.
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A practical guide to robust optimization
TL;DR: The aim of this paper is to help practitioners to understand robust optimization and to successfully apply it in practice, and to use many small examples to illustrate the discussions.
Sample Average Approximation Method for Chance Constrained Programming: Theory and Applications
TL;DR: This work considers the sample average approximation (SAA) approach and discusses the convergence properties of the resulting problem, and presents a method for constructing statistical lower bounds for the optimal value of the considered problem.
On Distributionally Robust Chance-Constrained Linear Programs
TL;DR: It is shown that, for a wide class of probability distributions on the data, the probability constraints can be converted explicitly into convex second-order cone constraints; hence the probability-constrained linear program can be solved exactly with great efficiency.
564
Data-driven chance constrained stochastic program
Ruiwei Jiang,Yongpei Guan +1 more
TL;DR: This paper derives an equivalent reformulation for DCC and shows that it is equivalent to a classical chance constraint with a perturbed risk level, and analyzes the relationship between the conservatism of D CC and the size of historical data, which can help indicate the value of data.
553
References
Programming under Probabilistic Constraint and Maximizing Probabilities under Constraints
András Prékopa
- 01 Jan 1995
TL;DR: In this article, the authors considered problems of the form ==================¯¯¯¯¯¯¯¯ $ \eqalign{ & {h_0}(x) = P\left( {{g_1}\left( {x,\xi } \right) \geqslant 0,{g_2}\left{x, \xi, \right} \right] \geqslant 0,...,{ g_r}\left[x, ξ], g 2(x, ǫ), g r} \left{h_
22
A regularized decomposition method for minimizing a sum of polyhedral functions
TL;DR: A new decomposition method that may start from an arbitrary point and simultaneously processes objective and feasibility cuts for each component and is finitely convergent without any nondegeneracy assumptions is proposed.
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