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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: Relations between the minimax, risk averse and nested formulations of multistage stochastic programming problems, and a minimax approach with moment constraints to the classical inventory model are studied.

102 citations

Journal ArticleDOI
TL;DR: This example indicates that for some mechanical engineering optimization problems, using the multicriterion optimization approach, the authors can automatically obtain a solution which is optimal and acceptable to the designer.

102 citations

Journal ArticleDOI
TL;DR: A Benders’ decomposition approach is proposed, and a greedy algorithm is employed to solve the recourse subproblem, and sets of valid inequalities are introduced to strengthen a relaxed master problem.
Abstract: We present a brief overview of four phases of nurse planning. For the last phase, which assigns nurses to patients, a stochastic integer programming model is developed. A Benders' decomposition approach is proposed to solve this problem, and a greedy algorithm is employed to solve the recourse subproblem. To improve the efficiency of the algorithm, we introduce sets of valid inequalities to strengthen a relaxed master problem. Computational results are provided based upon data from Baylor Regional Medical Center in Grapevine, Texas. Finally, areas of future research are discussed.

102 citations

Journal ArticleDOI
TL;DR: A novel robust fuzzy stochastic programming approach is proposed that has significant advantages in terms of mean value and variability of the objective function and the performance of the proposed model is compared with that of other models in term of the mean cost and variability by simulation.

102 citations

Proceedings ArticleDOI
23 Oct 2005
TL;DR: A new "demand-robust" model motivated by recent work on two-stage stochastic optimization problems is formed and a general structural lemma about special types of first-stage solutions for such problems is proved.
Abstract: Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worst-case among the various uncertain scenarios in the model. While these approaches may be thought of defining data- or cost-robust problems, we formulate a new "demand-robust" model motivated by recent work on two-stage stochastic optimization problems. We propose this in the framework of general covering problems and prove a general structural lemma about special types of first-stage solutions for such problems: there exists a first-stage solution that is a minimal feasible solution for the union of the demands for some subset of the scenarios and its objective function value is no more than twice the optimal. We then provide approximation algorithms for a variety of standard discrete covering problems in this setting, including minimum cut, minimum multi-cut, shortest paths, Steiner trees, vertex cover and un-capacitated facility location. While many of our results draw from rounding approaches recently developed for stochastic programming problems, we also show new applications of old metric rounding techniques for cut problems in this demand-robust setting.

102 citations


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