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<p>This study develops a comprehensive inventory model for deteriorating items with a power demand pattern, incorporating the effects of learning and completely backlogged shortages. The model addresses practical inventory management challenges where demand varies over time and all shortages are fully backordered, offering a useful framework for retailers. The learning effect accounts for gradual improvements in inventory handling and management, which contribute to lower operational costs and enhanced efficiency. To address uncertainty in real-world scenarios, the model is formulated in both crisp and fuzzy environments. In the fuzzy context, triangular fuzzy numbers and the signed distance method are applied for defuzzification. Analytical methods are utilized to determine the retailer’s optimal replenishment cycle, order quantity, and total cost. The convexity of the total cost function is illustrated by Mathematica 13.0.1 software, ensuring the feasibility of finding an optimal solution. Numerical examples are provided to validate the model, and a sensitivity analysis examines how key parameters influence the retailer’s decisions. The study also offers valuable managerial insights to support decision-making in inventory management. The model is particularly relevant for perishable products, such as foods and pharmaceuticals, where demand patterns and shortages are time-sensitive.</p>

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Retailer’s optimal strategy for deteriorating items with power demand and learning effect under complete backlogged shortages

  • 期刊论文

  • 发表日期：

    2025年7月19日

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  • Sourav Kumar Patra,
  • Susanta Kumar Paikray,
  • Umakanta Misra

摘要

This study develops a comprehensive inventory model for deteriorating items with a power demand pattern, incorporating the effects of learning and completely backlogged shortages. The model addresses practical inventory management challenges where demand varies over time and all shortages are fully backordered, offering a useful framework for retailers. The learning effect accounts for gradual improvements in inventory handling and management, which contribute to lower operational costs and enhanced efficiency. To address uncertainty in real-world scenarios, the model is formulated in both crisp and fuzzy environments. In the fuzzy context, triangular fuzzy numbers and the signed distance method are applied for defuzzification. Analytical methods are utilized to determine the retailer’s optimal replenishment cycle, order quantity, and total cost. The convexity of the total cost function is illustrated by Mathematica 13.0.1 software, ensuring the feasibility of finding an optimal solution. Numerical examples are provided to validate the model, and a sensitivity analysis examines how key parameters influence the retailer’s decisions. The study also offers valuable managerial insights to support decision-making in inventory management. The model is particularly relevant for perishable products, such as foods and pharmaceuticals, where demand patterns and shortages are time-sensitive.