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.
Genetic Algorithms, Evolutionary Algorithms, Scheduling
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
MIC 2011: Ninth Meta-heuristics International Conference, 25-28 July 2011
City or Country
Kimbrough, Steven O.; KUO, Ann; and LAU, Hoong Chuin.
Finding robust-under-risk solutions for flowshop scheduling. (2011). MIC 2011: Ninth Meta-heuristics International Conference, 25-28 July 2011. 1-10. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1387
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