AI Driven Inventory Optimization Framework Using Deep Learning and ‎Metaheuristic Algorithms

  • Authors

    • T Manivannan Department of Computer Science, St Joseph's University, Bengaluru, Karnataka, India
    • K. Deepa Department of CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, India‎
    • A. Devendran School of Business, Woxsen University, Hyderabad, Telangana, India
    • Gowri Sreelakshmi Neeli Department of Computer Applications, Aditya University, Surampalem, Andhra Pradesh, India
    • Dipesh Uike Department of MBA, Dr. Ambedkar Institute Of Management Studies And Research, Nagpur, Maharashtra, India
    • Clara Shanthi D School of CS and IT, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India
    • Preethi D School of CS and IT, JAIN (Deemed-to-be University), Bengaluru, Karnataka, India
    • Sumit Chaudhary Department of CSE, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun, Uttarakhand, India
    • H. ‎Mickle Aancy Department of MBA, Panimalar Engineering College, Chennai, Tamil Nadu, India
    • R. G. Vidhya Department of ECE, HKBK College of Engineering, Bangalore, India
    https://doi.org/10.14419/vp1eee47

    Received date: July 1, 2025

    Accepted date: August 4, 2025

    Published date: August 14, 2025

  • K-Nearest Neighbour; Sine Cosine Algorithm; Tasmanian Devil Optimization; Mean Absolute Error; Simulated Annealing

  • 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.

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    Manivannan, T. ., Deepa , K. ., Devendran , A. ., Neeli, G. S. . ., Uike , D. ., D, C. S. ., D, P., Chaudhary , S. ., Aancy , H. ‎Mickle ., & Vidhya, R. G. . (2025). AI Driven Inventory Optimization Framework Using Deep Learning and ‎Metaheuristic Algorithms. International Journal of Basic and Applied Sciences, 14(4), 405-411. https://doi.org/10.14419/vp1eee47