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Economic Order Quantity Model-Based Optimized Fuzzy Nonlinear Dynamic Mathematical Schemes.

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Summary

This study introduces a fuzzy mathematics approach to optimize inventory costs by managing uncertain parameters like shortages and ordering costs. The adaptive economic order quantity model enhances prediction accuracy and system efficiency for better inventory control.

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Area of Science:

  • Operations Research
  • Applied Mathematics
  • Inventory Management

Background:

  • Inventory management faces challenges due to uncertain market conditions affecting costs like shortages, ordering, and degradation.
  • Traditional models struggle with volatile parameters, necessitating advanced methods for accurate forecasting and strategy.
  • Fuzzy mathematics offers a robust framework for handling uncertainty and imprecision in observational data.

Purpose of the Study:

  • To develop an adaptive and integrative economic order quantity (EOQ) model utilizing fuzzy mathematics.
  • To manage uncertain inventory parameters, enhancing system exactness and computational efficiency.
  • To optimize inventory costs through improved control and monitoring of company processes.

Main Methods:

  • Employing fuzzy numbers to represent principal expenditures and uncertain parameters.
  • Developing an adaptive and integrative economic order quantity model.
  • Utilizing the graded mean integration method for solution determination.
  • Conducting numerical and sensitivity analyses with visual graphical depictions.

Main Results:

  • The proposed fuzzy EOQ model effectively manages uncertain parameters in inventory systems.
  • The model demonstrates improved exactness and computing efficiency in inventory cost management.
  • Numerical and sensitivity analyses validate the model's performance and applicability.
  • The study provides a structure for optimizing inventory costs and enhancing system development.

Conclusions:

  • Fuzzy mathematics-informed methods are crucial for accurate predictions in volatile environments.
  • The developed fuzzy EOQ model offers a reliable approach to inventory cost optimization.
  • The integrative model enhances inventory system performance, control, and monitoring capabilities.