scispace - formally typeset
Search or ask a question
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
More filters
Journal ArticleDOI
TL;DR: Due to a linear programming formulation of the optimization problem the method presented in the paper assures finding the global optimum, hence it may be considered as the useful tool for verification of results obtained in other ways.
Abstract: The main purpose of the paper is to provide an easy-to-use code for topological optimization of the least weight trusses, written in the Mathematica programming language. The main idea of the presented approach consists in using a fixed ground structure and the linear programming formulation of the optimization problem. The solver is based on the fast interior point method. The strong effort is done to create the effective generator of the computational model utilizing the high regularity of the ground structure and the high sparsity of the geometric matrix. The efficiency and reliability of the algorithm is confirmed in several numerical tests. Due to a linear programming formulation of the optimization problem the method presented in the paper assures finding the global optimum, hence it may be considered as the useful tool for verification of results obtained in other ways. The appended complete Mathematica code of the program developed will be supplied by the Publisher on SpringerLink.

98 citations

Posted Content
12 Apr 2000
TL;DR: In this paper, a framework for automated optimization of stochastic simulation models using Response Surface Methodology is developed, which is especially intended for simulation models where the calculation of the corresponding response function is very expensive or time-consuming.
Abstract: textWe develop a framework for automated optimization of stochastic simulation models using Response Surface Methodology. The framework is especially intended for simulation models where the calculation of the corresponding stochastic response function is very expensive or time-consuming. Response Surface Methodology is frequently used for the optimization of stochastic simulation models in a non-automated fashion. In scientific applications there is a clear need for a standardized algorithm based on Response Surface Methodology. In addition, an automated algorithm is less time-consuming, since there is no need to interfere in the optimization process. In our framework for automated optimization we describe all choices that have to be made in constructing such an algorithm.

98 citations

Journal ArticleDOI
TL;DR: It is shown that if the information structure is stochastically nested, then an optimization problem is tractable, and in particular for LQG problems, the team optimal solution is linear, despite the lack of deterministic nestedness or partial nestedness.
Abstract: Solutions to decentralized stochastic optimization problems lead to recursions in which the state space enlarges with the time-horizon, thus leading to non-tractability of classical dynamic programming. A common joint information state supplied to each of the agents leads to a tractable recursion, as is evident in the one-step-delayed information sharing structure case or when deterministic nestedness in information holds when there is a causality relationship as in the case of partially nested information structure. However, communication requirements for such conditions require exchange of very large data noiselessly, hence these assumptions are generally impractical. In this paper, we present a weaker notion of nestedness, which we term as stochastic nestedness, which is characterized by a sequence of Markov chain conditions. It is shown that if the information structure is stochastically nested, then an optimization problem is tractable, and in particular for LQG problems, the team optimal solution is linear, despite the lack of deterministic nestedness or partial nestedness. One other contribution of this paper is that, by regarding the multiple decision makers as a single decision maker and using Witsenhausen's equivalent model for discrete-stochastic control, it is shown that the common state required need not consist of observations and it suffices to share beliefs on the state and control actions; a pattern we refer to as k-stage belief sharing pattern. We discuss the minimum amount of information exchange required to achieve such an information pattern for k =1. The information exchange needed is generally strictly less than what is needed for deterministic nestedness and is zero whenever stochastic nestedness applies. In view of nestedness, we present a discussion on the monotone values of information channels.

98 citations

Journal ArticleDOI
TL;DR: A novel branch and cut algorithm is developed where necessary non-anticipativity constraints that are unlikely to be active are removed from the initial formulation and only added if they are violated within the search tree.

98 citations


Network Information
Related Topics (5)
Optimization problem
96.4K papers, 2.1M citations
86% related
Scheduling (computing)
78.6K papers, 1.3M citations
85% related
Optimal control
68K papers, 1.2M citations
84% related
Supply chain
84.1K papers, 1.7M citations
83% related
Markov chain
51.9K papers, 1.3M citations
79% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532