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The Global ETD Search service is a free service for researchers
to find electronic theses and dissertations. This service is
provided by the
Networked Digital Library of Theses and Dissertations.
Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
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Showing 1 to 2 of 2 (0.101 seconds)| 1 |
Satisticing solutions for multiobjective stochastic linear programming problemsAdeyefa, Segun Adeyemi 06 1900 (has links)
Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact,
many real life problems ranging from portfolio selection to water resource management
may be cast into this framework.
There are severe limitations in objectivity in this field due to the simultaneous presence
of randomness and conflicting goals. In such a turbulent environment, the mainstay of
rational choice does not hold and it is virtually impossible to provide a truly scientific
foundation for an optimal decision.
In this thesis, we resort to the bounded rationality and chance-constrained principles to
define satisficing solutions for Multiobjective Stochastic Linear Programming problems.
These solutions are then characterized for the cases of normal, exponential, chi-squared
and gamma distributions.
Ways for singling out such solutions are discussed and numerical examples provided for
the sake of illustration.
Extension to the case of fuzzy random coefficients is also carried out. / Decision Sciences
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| 2 |
Satisficing solutions for multiobjective stochastic linear programming problemsAdeyefa, Segun Adeyemi 06 1900 (has links)
Multiobjective Stochastic Linear Programming is a relevant topic. As a matter of fact,
many real life problems ranging from portfolio selection to water resource management
may be cast into this framework.
There are severe limitations in objectivity in this field due to the simultaneous presence
of randomness and conflicting goals. In such a turbulent environment, the mainstay of
rational choice does not hold and it is virtually impossible to provide a truly scientific
foundation for an optimal decision.
In this thesis, we resort to the bounded rationality and chance-constrained principles to
define satisficing solutions for Multiobjective Stochastic Linear Programming problems.
These solutions are then characterized for the cases of normal, exponential, chi-squared
and gamma distributions.
Ways for singling out such solutions are discussed and numerical examples provided for
the sake of illustration.
Extension to the case of fuzzy random coefficients is also carried out. / Decision Sciences
|
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