Computational Mathematics Approaches for Dynamic Inventory Optimization
DOI:
https://doi.org/10.64137/3108-2637/IJMAR-V2I1P103Keywords:
Dynamic Inventory Optimization, Computational Mathematics, Supply Chain Management, Linear Programming, Dynamic Programming, Inventory Control, Metaheuristic Algorithms, Machine Learning, Demand ForecastingAbstract
Dynamic inventory optimization has become a critical research area in modern supply chain management due to fluctuating market demand, uncertain supply conditions, and increasing operational complexity. Computational mathematics provides powerful techniques for modeling, analyzing, and optimizing inventory systems in dynamic environments. This paper explores various computational mathematics approaches for dynamic inventory optimization, including linear programming, integer programming, dynamic programming, stochastic modeling, and metaheuristic algorithms. The study also examines the integration of machine learning and predictive analytics for improving demand forecasting and adaptive inventory control. A mathematical framework is proposed to minimize inventory-related costs while maintaining high service levels and operational efficiency. Experimental analysis and comparative evaluations demonstrate the effectiveness of computational optimization methods in reducing holding costs, avoiding stock shortages, and improving decision-making accuracy. The paper further discusses practical applications in retail, manufacturing, healthcare, and e-commerce sectors, along with current challenges and future research opportunities in intelligent inventory management systems.
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