Optimizing Inventory Management: A Fuzzy Approach to Economic Order Quantity with Constraints

Generally, in deriving the solution of the economic order quantity (EOQ) inventory model, we consider the deterioration rate, holding cost, and ordering cost as constant. But in the case of real-life problems, the above case is not constant but slightly disturbed from its original crisp value. In this paper, a fuzzy inventory model is developed considering deterioration rate, holding cost, and ordering cost as fuzzy variables. Because, in practice, it is not always easy to determine the rate of deterioration precisely. In most cases it is uncertain in nature; therefore, it becomes reasonable to consider the vagueness and uncertainty of the deterioration rate in a fuzzy environment. These variables are represented by the trapezoidal membership function. The function principle is applied to obtain an optimum total fuzzy cost along with optimum order quantity and optimum shortage quantity.

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.52783/eel.v15i2.3197
Primary Topic
Scheduling and Optimization Algorithms
Type
article
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Optimizing Inventory Management: A Fuzzy Approach to Economic Order Quantity with Constraints

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Scheduling and Optimization Algorithms
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Optimizing Inventory Management: A Fuzzy Approach to Economic Order Quantity with Constraints

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Abstract

Generally, in deriving the solution of the economic order quantity (EOQ) inventory model, we consider the deterioration rate, holding cost, and ordering cost as constant. But in the case of real-life problems, the above case is not constant but slightly disturbed from its original crisp value. In this paper, a fuzzy inventory model is developed considering deterioration rate, holding cost, and ordering cost as fuzzy variables. Because, in practice, it is not always easy to determine the rate of deterioration precisely. In most cases it is uncertain in nature; therefore, it becomes reasonable to consider the vagueness and uncertainty of the deterioration rate in a fuzzy environment. These variables are represented by the trapezoidal membership function. The function principle is applied to obtain an optimum total fuzzy cost along with optimum order quantity and optimum shortage quantity.

Bennett University (IN)
Decent work and economic growth
Openalex Percentile: Top 83%
Scheduling and Optimization Algorithms
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