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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.


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Journal ArticleDOI
01 Dec 1997
TL;DR: This paper studies a linear programming problem in which all its elements are defined as fuzzy sets, and shows how it is possible to address and solve linear programming problems with data given in a qualitative form, instead of the usual quantitative and precise way.
Abstract: Managers, decision makers, and experts dealing with optimization problems often have a lack of information on the exact values of some parameters used in their problems. To deal with this kind of imprecise data, fuzzy sets provide a powerful tool to model and solve these problems. This paper studies a linear programming (LP) problem in which all its elements are defined as fuzzy sets. Special cases of this general model are found and reproduced, and it is shown that they coincide with the particular problems proposed in the literature by different authors and distinct approaches. Solution methods are also provided. They show how it is possible to address and solve linear programming problems with data given in a qualitative form, instead of the usual quantitative and precise way.

111 citations

Journal ArticleDOI
TL;DR: An optimization model with an ability to reflect uncertainties present in water quality problems and the technique employed is chance constrained programming wherein probabilistic constraints in a water quality optimization problem are replaced with their deterministic equivalents.
Abstract: An optimization model with an ability to reflect uncertainties present in water quality problems is described. The technique employed is chance constrained programming wherein probabilistic constraints in a water quality optimization problem are replaced with their deterministic equivalents. The uncertainty inherent in the random elements of the problem is characterized using first-order uncertainty analysis for the case study described.

111 citations

Book
01 Jan 2001
TL;DR: In this article, the authors present a survey on the history of semi-infinite programming and its application in probability and statistics, as well as a discussion of some applications of LSIP to Probability and Statistics.
Abstract: Preface. Contributing Authors. Part I: History. 1. On the 1962-1972 Decade of Semi-Infinite Programming: A Subjective View K.O. Kortanek. Part II: Theory. 2. About Disjunctive Optimization I.I. Eremin. 3. On Regularity and Optimality in Nonlinear Semi-Infinite Programming A. Hassouni, W. Oettli. 4. Asymptotic Constraint Qualifications and Error Bounds for Semi-Infinite Systems of Convex Inequalities W. Li, I. Singer. 5. Stability of the Feasible Set Mapping in Convex Semi-Infinite Programming M.A. Lopez, et al. 6. On Convex Lower Level Problems in Generalized Semi-Infinite Optimization J.-J. Ruckmann, O. Stein. 7. On Duality Theory of Conic Linear Problems A. Shapiro. Part III: Numerical Methods. 8. Two Logarithmic Barrier Methods for Convex Semi-Infinite Problems L. Abbe. 9. First-Order Algorithms for Optimization Problems with a Maximum Eigenvalue/Singular Value Cost and or Constraints E. Polak. 10. Analytic Center Based Cutting Plane Method for Linear Semi-Infinite Programming S.-Y. Wu, et al. Part IV: Modeling and Applications. 11. On Some Applications of LSIP to Probability and Statistics M. Dall'Aglio. 12. Separation by Hyperplanes: A Linear Semi-Infinite Programming Approach M.A. Goberna, et al. 13. A Semi-Infinite Optimization Approach to Optimal Spline Trajectory Planning of Mechanical Manipulators C. Guarino Lo Bianco, A. Piazzi. 14. On Stability of Guaranteed Estimation Problems: Error Bounds for Information Domains and Experimental Design M.I. Gusev, S.A.Romanov. 15. Optimization under Uncertainty and Linear Semi-Infinite Programming: A Survey T. Leon, E. Vercher. 16. Semi-Infinite Assignment and Transportation Games J. Sanchez-Soriano, et al. 17. The Owen Set and the Core of Semi-Infinite Linear Production Situations S. Tijs, et al.

111 citations

Journal ArticleDOI
TL;DR: An algorithm for solving stochastic programs with simpleourse generated by a linear programming problem with stochastics coefficients and a specific loss function is described.
Abstract: In this paper we describe an algorithm for solving stochastic programs with simplerecourse, i.e.,generated by a linear programming problem with stochastic coefficients and a specific loss function ...

111 citations

Journal ArticleDOI
TL;DR: A review of the state of the art of systems analysis and optimization techniques developed in the field of water resources for the planning and management of a ground-water system can be found in this paper.
Abstract: The objective of this paper is to review the state of the art of systems analysis and optimization techniques developed in the field of water resources for the planning and management of a ground-water system. The areas reviewed include the following: ground-water management models, inverse solution techniques for parameter identification, and optimal experimental design methods. Emphasis is placed upon ground-water supply management models, as opposed to models used for ground-water quality management. The techniques that have been used in the optimization of ground-water management include: linear programming, mixed-integer and quadratic programming, differential dynamic programming, nonlinear programming, and simulation. The inverse problem of parameter identification pertains the optimal determination of model parameters using historical input and output observations. Because of data limitation in both quantity and quality, the inverse problem is inherently ill posed. This paper summarizes recent advances made in the inverse procedures and methods developed to alleviate the problems of instability and nonuniqueness of the identified parameters. The optimal experimental design problem addresses the issue of data requirements and optimal sampling strategies for the purpose of parameter identification. A criterion must be established for the optimal design of a pumping test. The fundamental concept of optimal experimental design and various criteria used for optimization are reviewed.

111 citations


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