Inventory optimization under tri phased demand with dual aging and controlled backlogging

Area of Science:

• Operations Research
• Supply Chain Management
• Inventory Optimization

Background:

• Seasonal and perishable goods require specialized inventory models due to fluctuating demand and product degradation.
• Existing models often fail to capture complex factors like product amelioration, deterioration, and nonlinear holding costs.
• Effective inventory management is crucial for reducing costs and enhancing the sustainability of these goods.

Purpose of the Study:

• To develop a unified inventory model for seasonal and perishable goods that minimizes total inventory costs.
• To improve the sustainability of inventory systems by accurately reflecting product attributes and demand dynamics.
• To incorporate advanced features like product amelioration, deterioration, price-sensitive demand, and preservation investments.

Main Methods:

• A mathematical model incorporating Weibull distribution for product deterioration and amelioration.
• Modeling seasonal demand using a trapezoidal function and holding costs with a cubic function.
• Integrating backlogging and investment in preservation strategies to mitigate deterioration.
• Utilizing a hybrid optimization approach combining the genetic algorithm (GA) and krill herd algorithm (KHA).

Main Results:

• The proposed model accurately captures realistic product behavior, including deterioration and amelioration.
• Numerical examples and simulations under various seasonal conditions validate the model's effectiveness.
• The hybrid GA-KHA optimization algorithm demonstrated stability, computational accuracy, and good convergence.
• The model successfully balances inventory costs, demand, and product quality.

Conclusions:

• The developed unified inventory model offers a practical and effective solution for managing seasonal and perishable goods.
• The integration of advanced mathematical techniques and hybrid optimization enhances inventory management efficiency and sustainability.
• The model's ability to handle complex variables like deterioration, demand fluctuations, and preservation investments makes it suitable for real-world applications.