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Satisticing solutions for multiobjective stochastic linear programming problems

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dc.contributor.advisor Luhandjula, M. K.
dc.contributor.author Adeyefa, Segun Adeyemi
dc.date.accessioned 2012-05-18T10:11:44Z
dc.date.available 2012-05-18T10:11:44Z
dc.date.issued 2011-06
dc.identifier.uri http://hdl.handle.net/10500/5703
dc.description.abstract 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. en
dc.format.extent 1 online resource (xi, 133 leaves)
dc.language.iso en en
dc.subject Multiobjective programming en
dc.subject Stochastic programming en
dc.subject Linear programming en
dc.subject Satisfying solution
dc.subject Chance constrained
dc.subject Expected value optimality/efficiency
dc.subject Variance optimality/efficiency
dc.subject Expected value and standard deviation optimality/efficiency
dc.subject Minimum risk optimality/efficiency
dc.subject Optimality/efficiency with given probabilities
dc.subject Fuzzy random variables
dc.subject Random closed sets
dc.subject Embedding theorem
dc.subject.ddc 519.72
dc.subject.lcsh Linear programming
dc.subject.lcsh Stochastic processes
dc.title Satisticing solutions for multiobjective stochastic linear programming problems en
dc.type Thesis en
dc.description.department Decision Sciences


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