Stackelberg solutions to stochastic two-level linear programming problems

This paper considers a two-level linear programming problem involving random variable coefficients to cope with hierarchical decision making problems under uncertainty. Two decision making models are provided to optimize the mean of the objective function value or to minimize the variance. It is sho...

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Bibliographic Details
Published in:2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision Making, Honolulu, HI, 1-5 April 2007 pp. 240 - 244
Main Authors: Katagiri, H., Ichiro, N., Sakawa, M., Kato, K.
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2007
Subjects:
Computational intelligence
Decision making
Delta modulation
Functional programming
Linear programming
Mathematical programming
Random variables
Region 5
Stochastic processes
Uncertainty
ISBN:9781424407026, 1424407028
Online Access:Get full text
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Summary:This paper considers a two-level linear programming problem involving random variable coefficients to cope with hierarchical decision making problems under uncertainty. Two decision making models are provided to optimize the mean of the objective function value or to minimize the variance. It is shown that the original problem is transformed into a deterministic problem. The computational methods are constructed to obtain the Stackelberg solution to the two-level programming problems. An illustrative numerical example is provided to understand the geometrical properties of the solutions
ISBN:9781424407026
1424407028
DOI:10.1109/MCDM.2007.369445