AI Driven Inventory Optimization Framework Using Deep Learning and Metaheuristic Algorithms
Year: 2025
Type: article
Abstract: Optimized stock management is a crucial step to supply chain improvement, reducing operation costs, and providing high Service levels. In this study, a New model using Simulated Annealing (SA) and a hybrid optimization approach is put forward for optimizing supply chain stock process. The SA algorithm continuously enhances inventory solutions endeavoring to find near-optimal setups that balance service levels and customers' requirements. First, the supply chain dataset was collected and preprocessed with K-Nearest Neighbour (KNN) imputation for handling missing values and z-score normalization for scaling. For handling class imbalance, data augmentation was carried out using Synthetic Minority Over-sampling Technique (SMOTE). Subsequently, Deep Maxout Network (DMN) was utilized to perform feature extraction in order to identify key patterns and relationships within the data. The inventory optimization problem was subsequently tackled using the SA algorithm enhanced by a hybrid optimization approach (SCTDO) that integrates the exploratory ability of the Sine Cosine Algorithm (SCA) and the refinement ability of the Tasmanian Devil Optimization (TDO) algorithm. The hybrid approach is robust at efficient exploration and exploitation of the solution space. The proposed model was implemented in Python and evaluated on different performance criteria like correlation coefficient, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and computation time. The proposed algorithm produced an RMSE value of 0.017, MAE of 0.013, computation time of 0.93, and a correlation coefficient of 0.983. Comparative study with other models also reflected the improved performance of the proposed Framework.
DOI: 10.14419/vp1eee47
Authors: T Manivannan, K. Deepa, A. Devendran, Gowri Sreelakshmi Neeli, Dipesh Uike, Clara Shanthi D, Preethi D, Sumit Chaudhary, H. Mickle Aancy, R. G. Vidhya
Language: English
Subfield: Management Information Systems
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4413380597