Comparison of deep and conventional machine learning models for prediction of one supply chain management distribution cost

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 15, 2024

Strategic supply chain management (SCM) is essential for organizations striving to optimize performance and attain their goals. Prediction of distribution cost (SCMDC) one branch SCM it's For this purpose, four machine learning algorithms, including random forest (RF), support vector (SVM), multilayer perceptron (MLP) decision tree (DT), along with deep using convolutional neural network (CNN), was used predict analyze SCMDC. A comprehensive dataset consisting 180,519 open-source data points make the structure each algorithm. Evaluation based on Root Mean Square Error (RMSE) Correlation coefficient (R2) show CNN model has high accuracy in SCMDC prediction than other models. The algorithm demonstrated exceptional test dataset, an RMSE 0.528 R2 value 0.953. Notable advantages CNNs include automatic hierarchical features, proficiency capturing spatial temporal patterns, computational efficiency, robustness variations, minimal preprocessing requirements, end-to-end training capability, scalability, widespread adoption supported by extensive research. These attributes position as preferred choice precise reliable predictions, especially scenarios requiring rapid responses limited resources.

Язык: Английский

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Effect of phosphorus fractions on benthic chlorophyll-a; Insight from the machine learning models

Ecological Informatics, Год журнала: 2025, Номер 85, С. 102990 - 102990

Опубликована: Янв. 5, 2025

Язык: Английский

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Transitional shale reservoir quality evaluation based on Random Forest algorithm—a case study of the Shanxi Formation, eastern Ordos Basin, China

Earth Science Informatics, Год журнала: 2025, Номер 18(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Research on carbonation percentage of carbonated recycled concrete fine aggregate: experimental investigation and machine learning prediction

Journal of Sustainable Cement-Based Materials, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Янв. 19, 2025

Язык: Английский

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Predictive Modeling of Volume and Biomass in Pinus pseudostrobus Using Machine Learning and Allometric Approaches

Forest Science and Technology, Год журнала: 2025, Номер unknown, С. 1 - 13

Опубликована: Янв. 30, 2025

Язык: Английский

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