Publication Type

Conference Paper

Version

Postprint

Publication Date

7-2011

Abstract

We propose and explore, in the context of benchmark problems for flowshop scheduling, a risk-based concept of robustness for optimization problems. This risk-based concept is in distinction to, and complements, the uncertainty-based concept employed in the field known as robust optimization. Implementation of our concept requires problem solution methods that sample the solution space intelligently and that produce large numbers of distinct sample points. With these solutions to hand, their robustness scores are easily obtained and heuristically robust solutions found. We find evolutionary computation to be effective for this purpose on these problems.

Keywords

Genetic Algorithms, Evolutionary Algorithms, Scheduling

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

MIC 2011: Ninth Meta-heuristics International Conference, 25-28 July 2011

First Page

1

Last Page

10

Publisher

MIC

City or Country

Udine, Italy

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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