Titlebook: Stochastic Versus Fuzzy Approaches to Multiobjective Mathematical Programming under Uncertainty; Roman Slowinski,Jacques Teghem Book 1990

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Multiple Objective Linear Programming Problems in the Presence of Fuzzy Coefficientsr a potential action to be a satisfying one are established. Efficient methods for singling out satisfying alternatives are also described together with illustrative numerical examples. We end up with a number of concluding remarks and indicate lines for further developments.

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’FLIP’: An Interactive Method for Multiobjective Linear Programming with Fuzzy Coefficientsst, when safety parameters have to be defined in the transformation phase, and second, when the associate deterministic problem is solved. At the latter level, the user is assisted by a graphical display of solutions.

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Multiobjective Programming under Uncertainty : Scope and Goals of the Bookly in long-term planning problems and programming of development strategies. The nature of these problems requires taking into account multiple objectives on the one hand, and various kinds of uncertainty, on the other hand. During the past decade, the development of . has been particularly fruitful

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Multiobjective Programming: Basic Concepts and Approachesive programming (MOP). Rather than precisely discussing some of the techniques and methods used in MOP (many references are provided), we present and comment the basic approaches underlying these methods.

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Stochastic Programming: Numerical Solution Techniques by Semi-Stochastic Approximation Methodsy using deterministic descent directions or more exact gradient estimations at certain iteration points. Several methods for the computation of more exact gradient estimators and deterministic (feasible) descent directions are presented.

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Overview of Different Approaches for Solving Stochastic Programming Problems with Multiple Objectiveion oriented models in a coherent and systematic way. Lack of such an approach is one of the objections raised to deterministic mathematical programming modelling. The requirement for a single objective or payoff functions is another objection; it can be argued that most decision makers usually have

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Application of “Strange” to Energy Studieshniques of stochastic linear programming. It has been mainly used for applications in the field of energy planning. The paper presents some of the published case studies applicable to nuclear and renewable energies. The paper also shows how extensions of the method towards non-linear functions and m