International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
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Volume 8 Issue 3
May-June 2026

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A Hybrid Deep learning and Reinforcement Learning frame work for Deteriorating Inventory system with Time-Dependent Demand and Behavior - Driven Partial Backlogging

Author(s) Ms. Ranu Yadav, Mr. Harshvardhan singh, Dr. Jaya kushwah
Country India
Abstract In this study, develops a hybrid intelligent inventory framework is developed model for deteriorating items with time-dependent demand and partial backlogging. In many real-world inventory systems, traditional models fail to capture dynamic demand patterns and adaptive decision-making requirements. To address this limitation, the proposed approach integrates deep learning and reinforcement learning within a unified framework. A Long Short-Term Memory (LSTM) network is employed to predict time-dependent demand based on historical data, enabling more accurate estimation of future requirements. The predicted demand is then incorporated into the inventory system, where reinforcement learning is used to determine optimal replenishment policies through a sequential decision-making process. The model also considers deterioration effects and behavior-driven partial backlogging to reflect realistic operational conditions. Numerical analysis is conducted to evaluate the performance of the proposed model, and the results demonstrate that the integration of predictive and adaptive mechanisms significantly reduces total inventory cost. Sensitivity analysis further confirms the robustness of the model under varying system parameters. The findings suggest that the proposed approach provides a flexible and efficient framework for inventory management, offering improved decision-making capability and cost optimization. The proposed framework is suitable for practical inventory environments with dynamics demand behavior.
Keywords Deep learning, Reinforcement Learning, Demand Forecasting, Inventory optimization, Time Dependent Demand
Field Mathematics
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-11
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E-ISSN 2582-2160
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