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<p>This study aims to formulate an optimal inventory model for deteriorating products under a price-sensitive, non-linear demand environment while incorporating the effects of deterioration, complete shortages, and learning on holding costs. The primary objective is to minimize the total inventory cost under conditions of uncertainty and real-world variability. The proposed model employs a log-gamma distribution to characterize deterioration rates, allows fully backlogged shortages, and integrates the learning effect into the holding cost structure. Mathematical models are developed for both crisp and fuzzy frameworks, enhancing the model’s applicability and robustness. To validate the theoretical framework, numerical examples are provided, demonstrating the convexity of the total cost function through simulations using Mathematica 13.0.1 software. In addition, a sensitivity analysis of key parameters is conducted to assess their impact on inventory performance. The findings reveal that incorporating learning effects and price-sensitive demand significantly reduces overall inventory costs. These results offer valuable managerial insights, supporting the development of more effective inventory management strategies in complex and uncertain business environments.</p>

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Optimal inventory strategies having price-sensitive demand with log-gamma deterioration under complete backlogged shortages and learning effect

  • 期刊论文

  • 发表日期：

    2025年7月8日

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  • Priyabrat Das,
  • Sourav Kumar Patra,
  • Susanta Kumar Paikray

摘要

This study aims to formulate an optimal inventory model for deteriorating products under a price-sensitive, non-linear demand environment while incorporating the effects of deterioration, complete shortages, and learning on holding costs. The primary objective is to minimize the total inventory cost under conditions of uncertainty and real-world variability. The proposed model employs a log-gamma distribution to characterize deterioration rates, allows fully backlogged shortages, and integrates the learning effect into the holding cost structure. Mathematical models are developed for both crisp and fuzzy frameworks, enhancing the model’s applicability and robustness. To validate the theoretical framework, numerical examples are provided, demonstrating the convexity of the total cost function through simulations using Mathematica 13.0.1 software. In addition, a sensitivity analysis of key parameters is conducted to assess their impact on inventory performance. The findings reveal that incorporating learning effects and price-sensitive demand significantly reduces overall inventory costs. These results offer valuable managerial insights, supporting the development of more effective inventory management strategies in complex and uncertain business environments.