Based on Deep Learning Methods End-To-End Multi-Cycle Replenishment Strategy Research

Ziheng Wang

doi:10.62051/ijcsit.v2n2.28

Authors

Ziheng Wang

DOI:

https://doi.org/10.62051/ijcsit.v2n2.28

Keywords:

Data-Driven, Deep Learning, Inventory Management, Replenishment Decisions, Cost Reduction.

Abstract

Based on the historical sales data and inventory data of a large retailer, this paper aims to output optimal replenishment decisions from a data-driven perspective, in order to reduce inventory-related costs. The main research body of this paper is the establishment of an end-to-end multi-cycle replenishment model, and a mathematical model of multi-cycle replenishment is constructed after a series of preprocessing of historical datasets. At the same time, an end-to-end multi-cycle replenishment deep learning model is built based on the mathematical model of multi-cycle replenishment, and the end-to-end label construction is carried out by using the preprocessed data set, so as to train the end-to-end multi-cycle replenishment deep learning model, and then compare it with other models.

Downloads

Download data is not yet available.

References

Cheng, F.; Sun, R. Inventory Demand Forecast Based on Gray Correlation Analysis and Time Series Neural Network Hybrid Model. In 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD); IEEE: Guilin, 2017; pp 2491–2496. https://doi.org/10.1109/FSKD.2017.8393167.

Punia, S.; Shankar, S. Predictive Analytics for Demand Forecasting: A Deep Learning-Based Decision Support System. Knowledge-Based Systems 2022, 258, 109956. https://doi.org/10.1016/j.knosys.2022.109956.

Drif, A.; Cherifi, H. MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering. Entropy 2022, 24 (8), 1084. https://doi.org/10.3390/e24081084.

Kilimci, Z. H.; Akyuz, A. O.; Uysal, M.; Akyokus, S.; Uysal, M. O.; Atak Bulbul, B.; Ekmis, M. A. An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain. Complexity 2019, 2019, 1–15. https://doi.org/10.1155/2019/9067367.

Giri, C.; Chen, Y. Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry. Forecasting 2022, 4 (2), 565–581. https://doi.org/10.3390/forecast4020031.

Mezzogori, D.; Zammori, F. An Entity Embeddings Deep Learning Approach for Demand Forecast of Highly Differentiated Products. Procedia Manufacturing 2019, 39, 1793–1800. https://doi.org/10.1016/j.promfg.2020.01.260.

Mohamadi, N.; Niaki, S. T. A.; Taher, M.; Shavandi, A. An Application of Deep Reinforcement Learning and Vendor-Managed Inventory in Perishable Supply Chain Management. Engineering Applications of Artificial Intelligence 2024, 127, 107403. https://doi.org/10.1016/j.engappai.2023.107403.

Deng, C.; Liu, Y. A Deep Learning-Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection. Wireless Communications and Mobile Computing 2021, 2021, 1–14. https://doi.org/10.1155/2021/9969357.

Boute, R. N.; Gijsbrechts, J.; Van Jaarsveld, W.; Vanvuchelen, N. Deep Reinforcement Learning for Inventory Control: A Roadmap. European Journal of Operational Research 2022, 298 (2), 401–412. https://doi.org/10.1016/j.ejor.2021.07.016.

Zhou, Y.; Guo, K.; Yu, C.; Zhang, Z. Optimization of Multi-Echelon Spare Parts Inventory Systems Using Multi-Agent Deep Reinforcement Learning. Applied Mathematical Modelling 2024, 125, 827–844. https://doi.org/10.1016/j.apm.2023.10.039.

Wang, Q.; Peng, Y.; Yang, Y. Solving Inventory Management Problems through Deep Reinforcement Learning. J. Syst. Sci. Syst. Eng. 2022, 31 (6), 677–689. https://doi.org/10.1007/s11518-022-5544-6.

Mediavilla, M. A.; Dietrich, F.; Palm, D. Review and Analysis of Artificial Intelligence Methods for Demand Forecasting in Supply Chain Management. Procedia CIRP 2022, 107, 1126–1131. https://doi.org/10.1016/j.procir.2022.05.119.

Dittrich, M.-A.; Fohlmeister, S. A Deep Q-Learning-Based Optimization of the Inventory Control in a Linear Process Chain. Prod. Eng. Res. Devel. 2021, 15 (1), 35–43. https://doi.org/10.1007/s11740-020-01000-8.

De Moor, B. J.; Gijsbrechts, J.; Boute, R. N. Reward Shaping to Improve the Performance of Deep Reinforcement Learning in Perishable Inventory Management. European Journal of Operational Research 2022, 301 (2), 535–545. https://doi.org/10.1016/j.ejor.2021.10.045.

Sadeghi, H.; Sadjadi, S.; Aryanezhad, M. An Improved Wagner-Whitin Algorithm. 38th International Conference on Computers and Industrial Engineering 2008 2008, 3, 3023–3029.