Topic
Stochastic programming
About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.
Papers published on a yearly basis
Papers
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TL;DR: This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains and solves the problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and develops a case study using data from the state of Mississippi.
132 citations
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TL;DR: A hierarchy of near-optimal polynomial disturbance-feedback control policies is introduced, and it is shown how these can be computed by solving a single semidefinite programming problem.
Abstract: In this paper, we propose a new tractable framework for dealing with linear dynamical systems affected by uncertainty, applicable to multistage robust optimization and stochastic programming. We introduce a hierarchy of near-optimal polynomial disturbance-feedback control policies, and show how these can be computed by solving a single semidefinite programming problem. The approach yields a hierarchy parameterized by a single variable (the degree of the polynomial policies), which controls the trade-off between the optimality gap and the computational requirements. We evaluate our framework in the context of three classical applications-two in inventory management, and one in robust regulation of an active suspension system-in which very strong numerical performance is exhibited, at relatively modest computational expense.
132 citations
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TL;DR: A novel distributed method for convex optimization problems with a certain separability structure based on the augmented Lagrangian framework is proposed and compares favorably to two augmentedlagrangian decomposition methods known in the literature.
Abstract: We propose a novel distributed method for convex optimization problems with a certain separability structure. The method is based on the augmented Lagrangian framework. We analyze its convergence and provide an application to two network models, as well as to a two-stage stochastic optimization problem. The proposed method compares favorably to two augmented Lagrangian decomposition methods known in the literature, as well as to decomposition methods based on the ordinary Lagrangian function.
132 citations
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TL;DR: In this paper, theoretical results are presented for several classes of mathematical programming problems that include: the general quadratic programming problem, and unconstrained and constrained optimization problems with polynomial terms in the objective function and/or constraints.
132 citations
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TL;DR: The network simplex algorithm, stochastic simulation and genetic algorithm are integrated to produce a hybrid intelligent algorithm to solve capacitated location-allocation problem with stochastically demands.
132 citations