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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


Papers
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Journal ArticleDOI
TL;DR: A stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability is formulated.
Abstract: In this paper, we consider a scenario where a group of investor-owned independently-operated storage units seek to offer energy and reserve in the day-ahead market and energy in the hour-ahead market. We are particularly interested in the case where a significant portion of the power generated in the grid is from wind and other intermittent renewable energy resources. In this regard, we formulate a stochastic programming framework to choose optimal energy and reserve bids for the storage units that takes into account the fluctuating nature of the market prices due to the randomness in the renewable power generation availability. We show that the formulated stochastic program can be converted to a convex optimization problem to be solved efficiently. Our simulation results also show that our design can assure profitability of the private investment on storage units. We also investigate the impact of various design parameters, such as the size and location of the storage unit on increasing the profit.

209 citations

Journal ArticleDOI
TL;DR: This paper presents and solves a single-period, multiproduct, downward substitution model that has one raw material as the production input and produces N different products as outputs and compares three different solution methods.
Abstract: in this paper, we present and solve a single-period, multiproduct, downward substitution model. Our model has one raw material as the production input and produces N different products as outputs. The demands and yields for the products are random. We determine the optimal production input and allocation of the N products to satisfy demands. The problem is modeled as a two-stage stochastic program, which we show can be decomposed into a parameterized network flow problem. We present and compare three different solution methods: a stochastic linear program, a decomposition resulting in a series of network flow subproblems, and a decomposition where the same network flow subproblems are solved by a new greedy algorithm.

209 citations

BookDOI
31 Jul 2012
TL;DR: This volume contains 16 chapters written by various leading researchers and presents a cohesive authoritative overview of developments and applications in their emerging field of optimization.
Abstract: In many decision processes there is an hierarchy of decision-makers and decisions are taken at different levels in this hierarchy. Multilevel programming focuses on the whole hierarchy structure. In terms of modeling, the constraint domain associated with a multilevel programming problem is implicitly determined by a series of optimization problems which must be solved in a predetermined sequence. The field of multilevel optimization has become a well-known and important research field. Hierarchical structures can be found in scientific disciplines such as environment, ecology, biology, chemical engineering, mechanics, classification theory, databases, network design, transportation, game theory and economics. Moreover, new applications are constantly being introduced. This has stimulated the development of new theory and efficient algorithms. This volume contains 16 chapters written by various leading researchers and presents a cohesive authoritative overview of developments and applications in their emerging field of optimization. Audience: Researchers whose work involves the application of mathematical programming and optimization to hierarchical structures.

209 citations

Journal ArticleDOI
TL;DR: Basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastically programming problems are reviewed.
Abstract: Stochastic programming problems have very large dimension and characteristic structures which are tractable by decomposition. We review basic ideas of cutting plane methods, augmented Lagrangian and splitting methods, and stochastic decomposition methods for convex polyhedral multi-stage stochastic programming problems.

209 citations

Proceedings ArticleDOI
01 Dec 1999
TL;DR: This tutorial is not meant to be an exhaustive literature search on simulation optimization techniques, but its emphasis is mostly on issues that are specific to simulation optimization.
Abstract: Simulation models can be used as the objective function and/or constraint functions in optimizing stochastic complex systems. This tutorial is not meant to be an exhaustive literature search on simulation optimization techniques. It does not concentrate on explaining well-known general optimization and mathematical programming techniques either. Its emphasis is mostly on issues that are specific to simulation optimization. Even though a lot of effort has been spent to provide a reasonable overview of the field, still there are methods and techniques that have not been covered and valuable works that may not have been mentioned.

207 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532