نتایج جستجو برای: robust optimization
تعداد نتایج: 509147 فیلتر نتایج به سال:
In this paper, Mond-Weir type duality results for a uncertain multiobjective robust optimization problem are given under generalized invexity assumptions. Also, weak vector saddle-point theorems are obtained under convexity assumptions.
We study the dual problems associated with the robust counterparts of uncertain convex programs. We show that while the primal robust problem corresponds to a decision maker operating under the worst possible data, the dual problem corresponds to a decision maker operating under the best possible data. © 2008 Elsevier B.V. All rights reserved.
modeling of fermentation processes is so complicated and uncertain; therefore it is necessary to provide a robust and appropriate dynamic optimization method. in order to obtain the maximum amount of yeast (saccharomyces cerevisiae), the bioreactor must be operated under optimal conditions. to determine substrate feeding in a fed-batch bioreactor, a simulated annealing (sa) approach was examine...
Sustainability in agricultural is determined by aspects like economy, society and environment. Multi-objective programming (MOP) model has been a widely used tool for studying and analyzing the sustainability of agricultural system. However, optimization models in most applications are forced to use data which is uncertain. Recently, robust optimization has been used as an optimization model th...
Many portfolio optimization problems deal with allocation of assets which carry a relatively high market price. Therefore, it is necessary to determine the integer value of assets when we deal with portfolio optimization. In addition, one of the main concerns with most portfolio optimization is associated with the type of constraints considered in different models. In many cases, the resulted p...
We propose a distributionally robust optimization formulation with Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data both linear regression and classification problems. The resulting model offers robustness explanations least absolute shrinkage selection operator algorithms highlights connection between regularization. prove probabilistic bounds o...
Distributionally robust optimization is a paradigm for decision-making under uncertaintywhere the uncertain problem data is governed by a probability distribution that is itself subjectto uncertainty. The distribution is then assumed to belong to an ambiguity set comprising alldistributions that are compatible with the decision maker’s prior information. In this paper,we propose...
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