Discrete Optimization Problems with Interval Data: Pareto Set of Solutions or Set of Weak Solutions?

Discrete Optimization Problems with Interval Data: Pareto Set of Solutions or Set of Weak Solutions? For optimization problems with interval uncertainty, traditionally researchers have considered definitions based on element-wise optimality: a feasible solution is a strong solution if it optimizes the objective function f(x, c) for all possible values of the parameters c within the given intervals c, and it is a weak solution if it optimizes the objective function f(x, c) for some values c∈c. In our previous papers, we introduced the alternative approach based on the idea of Pareto optimality. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Reliable Computing Springer Journals

Discrete Optimization Problems with Interval Data: Pareto Set of Solutions or Set of Weak Solutions?

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Publisher
Kluwer Academic Publishers
Copyright
Copyright © 2004 by Kluwer Academic Publishers
Subject
Mathematics; Numeric Computing; Approximations and Expansions; Computational Mathematics and Numerical Analysis; Mathematical Modeling and Industrial Mathematics
ISSN
1385-3139
eISSN
1573-1340
D.O.I.
10.1023/B:REOM.0000047095.22096.16
Publisher site
See Article on Publisher Site

Abstract

For optimization problems with interval uncertainty, traditionally researchers have considered definitions based on element-wise optimality: a feasible solution is a strong solution if it optimizes the objective function f(x, c) for all possible values of the parameters c within the given intervals c, and it is a weak solution if it optimizes the objective function f(x, c) for some values c∈c. In our previous papers, we introduced the alternative approach based on the idea of Pareto optimality.

Journal

Reliable ComputingSpringer Journals

Published: Nov 19, 2004

References

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