Table 1 From Strong Formulations For Distributionally Robust Chance Constrained Programs With

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Distributionally Robust Chance-constrained Markov Decision Processes – Faculty Of Science And ...

Distributionally Robust Chance-constrained Markov Decision Processes – Faculty Of Science And ... We demonstrate the computational efficacy of our proposed formulations on two classes of problems, namely stochastic portfolio optimization and resource planning. The formulation (7) for (saa) is well studied in the literature, and many classes of valid inequalities for the formulation have been developed; see e.g., [1, 16, 17, 20, 22, 23, 25, 34, 35].

(PDF) Kernel Distributionally Robust Chance-constrained Process Optimization

(PDF) Kernel Distributionally Robust Chance-constrained Process Optimization We demonstrate the computational efficacy of our proposed formulations on two classes of problems, namely stochastic portfolio optimization and resource planning. In this paper, we consider mathematical programs with distributionally robust chance constraints (mpdrcc), where the ambiguity set is given by the general moment information. from the contaminated data driven viewpoint, we first study the qualitative statistical robustness of mpdrcc. In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. a usual approach in this setting is to enforce the constraints up to a given level of probability. This paper studies in detail the formulation for distributionally robust chance constrained programs under wasserstein ambiguity, focusing on the case of linear safety sets with right hand side uncertainty.

(PDF) Distributionally Robust Chance Constrained P-Hub Center Problem

(PDF) Distributionally Robust Chance Constrained P-Hub Center Problem In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. a usual approach in this setting is to enforce the constraints up to a given level of probability. This paper studies in detail the formulation for distributionally robust chance constrained programs under wasserstein ambiguity, focusing on the case of linear safety sets with right hand side uncertainty. Our main result concerns the following mip formulation for the joint chance constraint of (dr ccp) where s(x) can be either the open or the closed safety set from (6):. This work presents a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution by studying a scheme that effectively combines these approximations via variable fixing. We first review recent developments in mixed integer linear formulations of chance constrained programs that arise from finite discrete distributions (or sample average approximation). In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. a usual approach in this setting is to enforce the constraints up to a given level of probability.

(PDF) Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage ...

(PDF) Tractable Data Enriched Distributionally Robust Chance-Constrained Conservation Voltage ... Our main result concerns the following mip formulation for the joint chance constraint of (dr ccp) where s(x) can be either the open or the closed safety set from (6):. This work presents a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution by studying a scheme that effectively combines these approximations via variable fixing. We first review recent developments in mixed integer linear formulations of chance constrained programs that arise from finite discrete distributions (or sample average approximation). In this paper, we discuss linear programs in which the data that specify the constraints are subject to random uncertainty. a usual approach in this setting is to enforce the constraints up to a given level of probability.