4 edition of **Stochastic versus fuzzy approaches to multiobjective mathematical programming under uncertainty** found in the catalog.

- 85 Want to read
- 36 Currently reading

Published
**1990**
by Kluwer Academic in Dordrecht, Boston
.

Written in English

- Programming (Mathematics),
- Fuzzy systems.,
- Stochastic processes.

**Edition Notes**

Includes bibliographical references and index.

Statement | edited by Roman Slowinski, Jacques Teghem. |

Series | Theory and decision library., v. 6 |

Contributions | Słowiński, Roman., Teghem, Jacques, 1948- |

Classifications | |
---|---|

LC Classifications | QA402.5 .S74 1990 |

The Physical Object | |

Pagination | viii, 426 p. : |

Number of Pages | 426 |

ID Numbers | |

Open Library | OL1882039M |

ISBN 10 | 0792308875 |

LC Control Number | 90042735 |

Stochastic LP model is being referred as LP under uncertainty. Fuzzy AHP is being referred as MCDM model under uncertainty. But the approach to solution are different. R. Slowinski and J. Teghem, Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty, Kluwer Academic, Dordrecht, doi: / Google Scholar [31]Cited by: 1.

Fuzzy programming deals with mathematical programming problems under non-probabilistic uncertainty. The idea of fuzzy programming was first given by R.E. Bellman and L.A. Zadeh and then developed by H. Tanaka and H.-J. approaches treat soft constraints and vagueness of aspiration levels of objective function values and are called flexible programming. An interval-parameter fuzzy linear programming method (IFMOLP) is proposed in this study for multiple objective decision-making under uncertainty. As a hybrid of interval-parameter and fuzzy methodologies, the IFMOLP incorporates interval-parameter linear programming and fuzzy multiobjective programming approaches to form an integrated optimization system. The method .

Downloadable! In this work, we deal with obtaining efficient solutions for stochastic multiobjective programming problems. In general, these solutions are obtained in two stages: in one of them, the stochastic problem is transformed into its equivalent deterministic problem, and in the other one, some of the existing generating techniques in multiobjective programming are applied to obtain. Fuzzy versus stochastic approaches to multicriteria linear programming under uncertainty Fuzzy versus stochastic approaches to multicriteria linear programming under uncertainty Slowinski, R.; Teghem, J. Recently, both authors independently proposed two different approaches to multicriteria linear programming under uncertainty with a view of an application to .

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Lastly the constraints, expressed by equalities or inequalities between linear expressions, are often softer in reality that what their mathematical expression might let us believe, and infeasibility as detected by the linear programming techniques can often been coped with by.

Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty by Roman Slowinski,available at Book Depository with free delivery worldwide. Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty: Roman Slowinski: /5(1).

Chapter 1. Multiobjective programming under uncertainty: scope and goals of the book. 3 R. SLOWINSKI (PL), J. TEGHEM (B) Chapter 2. Multiobjective programming: basic concepts and approaches. 7 D. VANDERPOOTEN (F) Chapter 3. Stochastic programming: numerical Solution technigues by semi-stochastic approximation methods.

23 K. MARTI (D) Chapter 4. Slowinski R., Teghem J. () Multiobjective Programming under Uncertainty: Scope and Goals of the Book. In: Slowinski R., Teghem J. (eds) Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty.

Theory and Decision Library (Series D: System Theory, Knowledge Engineering and Problem Solving), vol : Roman Slowinski, Jacques Teghem. Multiobjective stochastic linear programming with incomplete information: a general methodology.- 5.

Computation of efficient solutions of stochastic optimization problems with applications to regression and scenario analysis The uncertainty is modeled using random variables (stochastic programming) or fuzzy variables (possibilistic programming). The solution is the undominated set for a multiobjective problem associated with a game against by: 2.

Special stress is placed on interactive decision making aspects of fuzzy stochastic multiobjective programming for human-centered systems under uncertainty in most realistic situations when dealing with both fuzziness and randomness. with decision problems involving uncertainty, stochastic pro-gramming approaches and fuzzy programming approaches have been developed.

In stochastic programming approaches [1],[2],[4],[7], two stage problems and chance constrained programming models have been investigated in various ways, and they were extended to multiobjective stochastic. Slowinski, J. Teghem (Eds.), Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty, Kluwer Academic Publishers, Dordrecht (), pp.

Google ScholarCited by: R. Slowinski, J. Teghem (Eds.), Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty, Kluwer Academic Publishers, Dordrecht (), pp.

Google Scholar [5]Cited by: K. Marti, Stochastic programming: Numerical solution techniques by semi-stochastic approximation methods, in: R. Slowinski and J. Teghem, Eds., Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty Cited by: In the first part, the general history and the approach of fuzzy mathematical programming are introduced.

Using a numerical example, some models of fuzzy linear programming are described. In the second part of the paper, fuzzy mathematical programming approaches are compared to stochastic programming by: Although several books or monographs on multiobjective optimization under uncertainty have been published, there seems to be no book which starts with an introductory chapter of linear programming and is designed to incorporate both fuzziness and randomness into multiobjective programming in.

Czyżak P. () Application of the ’FLIP’ Method to Farm Structure Optimization under Uncertainty. In: Slowinski R., Teghem J.

(eds) Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty. Theory and Decision Library (Series D: System Theory, Knowledge Engineering and Problem Solving), vol by: (). Stochastic Programming with Multiple Objective Functions.

Stochastic Programming. Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming Under Uncertainty. The Vectorial Minimum Risk Problem. Theory of Multiobjective. Although studies on multiobjective mathematical programming under uncertainty have been accumulated and several books on multiobjective mathematical programming under uncertainty.

This book focuses on how to model decision problems under uncertainty using models from stochastic programming. Different models and their properties are discussed on a conceptual level.

The book is intended for graduate students, who have a solid background in mathematics. The General Framework.- 1. Multiobjective programming under uncertainty: scope and goals of the book.- 2. Multiobjective programming: basic concepts and approaches.- 3.

Stochastic programming. For decision making problems involving uncertainty, there exist two typical approaches: a stochastic programming based on the probability theory and a fuzzy programming based on the fuzzy set.

AbstractIn this paper, a likely situation of a set of decision maker’s with. STRANGE an interactive method for multiobjective stochastic linear programming and STRANGE-MOMIX its extension to integer variables.

In R. Slowinski and J. Teghem, editors, Stochastic versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty, pagesKluwer Academic, Dordrecht.I. The General Framework.- 1. Multiobjective programming under uncertainty: scope and goals of the book.- 2.

Multiobjective programming: basic concepts and approaches.- 3. Stochastic programming Author: Fatima Bellahcene.Stochastic Programming: Numerical Solution Techniques by Semi-Stochastic Approximation Methods. Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty, Cited by: