Finding robust-under-risk solutions for flowshop scheduling

• Main Authors: Kimbrough, Steven O., KUO, Ann, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2011
• Subjects: Genetic Algorithms; Evolutionary Algorithms; Scheduling; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/1387; https://ink.library.smu.edu.sg/context/sis_research/article/2386/viewcontent/robustflowshop_MIC.pdf
• Institution: Singapore Management University

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.