Abstract
This paper proposes a covariance matrix adaptation evolution strategy (CMAES) based algorithm for a robotic flow shop scheduling problem with multiple robots and parallel machines. The algorithm is compared to three popular scheduling rules as well as existing schedules at a South African anodising plant. The CMAES algorithm statistically significantly outperformed all other algorithms for the size of problems currently scheduled by the anodising plant. A sensitivity analysis was also conducted on the number of tanks required at critical stages in the process to determine the effectiveness of the CMAES algorithm in assisting the anodising plant to make business decisions.
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Acknowledgements
This work is based on the research supported wholly or in part by the National Research Foundation of South Africa (Grant Number 109273). The authors would also like to thank the University of Twente for their financial support.
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Behr, C.M., Grobler, J. (2018). Robotic Flow Shop Scheduling with Parallel Machines and No-Wait Constraints in an Aluminium Anodising Plant with the CMAES Algorithm. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2018. Lecture Notes in Computer Science(), vol 10841. Springer, Cham. https://doi.org/10.1007/978-3-319-91253-0_29
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