Abstract
Thanks to new technologies, it is possible to make an automatic robotic treatment of plants for the mildew in greenhouses. The optimization of the scheduling of this robotic treatment presents a real challenge due to the continue evolution of disease level. The conventional optimization methods can not provide an accurate scheduling capable to eliminate the disease from the greenhouse. This paper proposes a solution to provide a dynamic scheduling problem of evolutionary tasks in horticulture. We first developed a genetic algorithm (GA) for a static model. Then we improved it for the dynamic case where a dynamic genetic algorithm (DGA) based on the prediction of the task amount is developed. To test the performance of the designed algorithms, especially for the dynamic case, we integrated our algorithms in a simulator.
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Acknowledgment
This research was possible thanks to €1.35 million financial support from the European Regional Development Fund provided by the Interreg North-West Europe Program in context of UV-Robot project.
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Mazar, M., Bettayeb, B., Klement, N., Sahnoun, M., Louis, A. (2021). Dynamic Scheduling of Robotic Mildew Treatment by UV-c in Horticulture. In: Borangiu, T., Trentesaux, D., Leitão, P., Cardin, O., Lamouri, S. (eds) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. SOHOMA 2020. Studies in Computational Intelligence, vol 952. Springer, Cham. https://doi.org/10.1007/978-3-030-69373-2_36
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