Automatic execution algorithm of supply chain intelligent operation robot scheduling logic based on machine learning

Xiang Li; Mingxiang Yang; Bing Shen; Jingyi Li; Bingling Gu

doi:10.1117/12.3013595

29 November 2023 Automatic execution algorithm of supply chain intelligent operation robot scheduling logic based on machine learning

Xiang Li, Mingxiang Yang, Bing Shen, Jingyi Li, Bingling Gu

Author Affiliations +

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370W (2023) https://doi.org/10.1117/12.3013595
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

This paper proposes a machine learning-based automatic execution algorithm, aiming to optimize the efficiency of supply chain operation. The external conditions for the robot to complete the task are set, and the objective function is constructed based on the task completion cost and time, from which the scheduling logic of the supply chain intelligent operation robot is constructed. The genetic algorithm in machine learning is used to solve the mathematical model, and the fitness function is set as the reciprocal of the objective function, executed until the minimum operation cost and time, and outputs the automatic execution scheme of the robot scheduling logic. Through experimental verification, the results show that the algorithm can effectively improve the efficiency of supply chain operation, reduce the cost, and have high adaptability and accuracy. This algorithm provides a feasible automation solution for supply chain intelligent operation robot scheduling.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiang Li, Mingxiang Yang, Bing Shen, Jingyi Li, and Bingling Gu "Automatic execution algorithm of supply chain intelligent operation robot scheduling logic based on machine learning", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370W (29 November 2023); https://doi.org/10.1117/12.3013595

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