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<p>In real-world scenarios, the deterioration rate of inventory items is typically considered an uncontrollable factor. However, with the application of suitable preservation technologies, deterioration can be mitigated to a certain extent. Additionally, suppliers often provide permissible delays in payment to encourage higher sales volumes. This study presents an EOQ-based inventory model that integrates power-pattern demand, trade credit arrangements, partial backlogging of shortages, and deliberate investment in preservation technology. To enhance realism, the model integrates a learning effect on holding costs, reflecting the gradual reduction in inventory-related expenses as experience increases. The proposed model is mathematically formulated and solved under both crisp and fuzzy environments, accommodating both deterministic and uncertain scenarios. The primary objective is to determine the optimal preservation investment cost and cycle time to maximize overall profit. Numerical examples are analyzed to validate the model, and the concavity of the profit function is demonstrated using Mathematica 13.0.1 software. A comprehensive sensitivity analysis is conducted to examine the influence of key parameters, providing actionable managerial insights. These insights aim to support inventory managers in optimizing strategies for items with variable demand and controllable deterioration rates under trade credit policies.</p>

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Optimal Inventory Policy having Power Pattern Demand with Preservation Technology Investment and Permissible Delay in Payment under Allowable Shortages

  • 期刊论文

  • 发表日期：

    2025年12月26日

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  • Sourav Kumar Patra,
  • Susanta Kumar Paikray,
  • Boina Anil Kumar

摘要

In real-world scenarios, the deterioration rate of inventory items is typically considered an uncontrollable factor. However, with the application of suitable preservation technologies, deterioration can be mitigated to a certain extent. Additionally, suppliers often provide permissible delays in payment to encourage higher sales volumes. This study presents an EOQ-based inventory model that integrates power-pattern demand, trade credit arrangements, partial backlogging of shortages, and deliberate investment in preservation technology. To enhance realism, the model integrates a learning effect on holding costs, reflecting the gradual reduction in inventory-related expenses as experience increases. The proposed model is mathematically formulated and solved under both crisp and fuzzy environments, accommodating both deterministic and uncertain scenarios. The primary objective is to determine the optimal preservation investment cost and cycle time to maximize overall profit. Numerical examples are analyzed to validate the model, and the concavity of the profit function is demonstrated using Mathematica 13.0.1 software. A comprehensive sensitivity analysis is conducted to examine the influence of key parameters, providing actionable managerial insights. These insights aim to support inventory managers in optimizing strategies for items with variable demand and controllable deterioration rates under trade credit policies.