Abstract
Developing efficient raw material ordering and transshipment strategies for companies with uncertain supply has attracted extensive interests from both academic and industrial researchers. Some methods have been proposed, such as obtaining a strategy using a heuristic algorithm, or developing an ordering scheme and a transportation scheme separately. These methods can work in some cases, but they can also lead to local optimization. To address this problem, we proposed the TGPFM framework, which takes raw material ordering, transshipment, and inventory into account. The TGPFM is made up of a supply capacity grey cycle prediction model, a transporter time series prediction model, a supplier PCA evaluation model, a multi-objective ordering scheme planning model, and a transshipment planning model. As a result, the problem of local optimization, which is induced by considering each process separately, can be effectively avoided. We conducted experiments on data from national competitions to verify the framework’s validity. The results show that putting a weight limit on inventory and material types in the ordering model, as well as using a PCA-based supplier ranking table, can help get a better overall plan. The time series of transit loss outperforms the grey prediction, and the grey prediction model combined with the excess fluctuation function can better predict supplier supply quantity.
This work was supported by National Natural Science Foundation of China (No.62102107, 62072132, 62002074, 62072127, 62002076).
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Hu, D., Li, W., Yu, Y., Li, J., Yan, H. (2023). TGPFM: An Optimized Framework for Ordering and Transporting Raw Materials for Production. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13657. Springer, Cham. https://doi.org/10.1007/978-3-031-20102-8_26
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