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
More filters
••
TL;DR: In this paper, the authors considered a large number of scenarios and formulated the wind power investment problem as a mathematical program with equilibrium constraints, and proposed a Benders decomposition algorithm to solve the problem.
Abstract: Investment in wind power facilities involves a high level of uncertainty. To properly model such uncertainty, we consider a large number of scenarios and formulate this investment problem as a mathematical program with equilibrium constraints. The target of this problem is to maximize the profit from wind power investment in a target year, and it is subject to complementarity constraints describing a large number of market clearing conditions. Since the profit as a function of the investment variables has as sufficiently convex envelope, the considered problem can be solved by Benders decomposition. Thus, we propose, describe, and analyze a Benders decomposition algorithm to efficiently tackle the wind power investment problem.
107 citations
••
TL;DR: This paper considers several portfolio selection problems including probabilistic future returns with ambiguous expected returns assumed as random fuzzy variables, and their efficient solution methods to find a global optimal solution of each problem is constructed.
107 citations
••
TL;DR: Results indicate that the proposed energy management strategy can greatly improve the fuel economy and be employed in real-time when compared with the stochastic dynamic programming and conventional RL approaches.
107 citations
••
TL;DR: In this article, a stochastic version of the classical multi-item Capacitated Lot-Sizing Problem (CLSP) is considered, where demand uncertainty is explicitly modeled through a scenario tree.
Abstract: We consider a stochastic version of the classical multi-item Capacitated Lot-Sizing Problem (CLSP). Demand uncertainty is explicitly modeled through a scenario tree, resulting in a multi-stage mixed-integer stochastic programming model with recourse. We propose a plant-location-based model formulation and a heuristic solution approach based on a fix-and-relax strategy. We report computational experiments to assess not only the viability of the heuristic, but also the advantage (if any) of the stochastic programming model with respect to the considerably simpler deterministic model based on expected value of demand. To this aim we use a simulation architecture, whereby the production plan obtained from the optimization models is applied in a realistic rolling horizon framework, allowing for out-of-sample scenarios and errors in the model of demand uncertainty. We also experiment with different approaches to generate the scenario tree. The results suggest that there is an interplay between different manager...
107 citations
••
TL;DR: This paper addresses energy consumption scheduling in a distribution network with connected microgrids consisting of a local area with a determined demand and neighboring areas with an uncertain demand and an adaptive scheduling approach provided with online stochastic iterations to capture the randomness of the uncertain demand over time.
Abstract: Energy consumption scheduling to achieve low-power generation cost and a low peak-to-average ratio is a critical component in distributed power networks. Implementing such a component requires the knowledge of the whole power demand throughout the network. However, due to the diversity of power demands, this requirement is not always satisfied in practical scenarios. To address this inconsistency, this paper addresses energy consumption scheduling in a distribution network with connected microgrids consisting of a local area with a determined demand and neighboring areas with an uncertain demand. The total cost and peak-to-average ratio minimizations are formulated as a multi objective optimization problem. In addition to a deterministic optimal solution, an adaptive scheduling approach is provided with online stochastic iterations to capture the randomness of the uncertain demand over time. Numerical results demonstrate the effectiveness of the proposed adaptive scheduling schemes in the following results obtained from optimal solutions.
107 citations