Abstract
This chapter investigates the multitask dynamic scheduling problems where different tasks have different and unknown relatedness. This chapter shows how to measure the relatedness between dynamic scheduling tasks, and how to use the relatedness information to choose assisted task to enhance positive knowledge transfer between tasks. The results show that the proposed task relatedness measure can detect related tasks effectively and sharing knowledge between related tasks can help learn effective scheduling heuristics for a task. In addition, the relatedness between tasks and the selected assisted tasks are also analysed. Last, the factors that contribute to the effectiveness improvement are studied.
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Zhang, F., Nguyen, S., Mei, Y., Zhang, M. (2021). Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling. In: Genetic Programming for Production Scheduling. Machine Learning: Foundations, Methodologies, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-16-4859-5_14
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DOI: https://doi.org/10.1007/978-981-16-4859-5_14
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Publisher Name: Springer, Singapore
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Online ISBN: 978-981-16-4859-5
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