An Application of Monte–Carlo Simulation in Probabilistic Inventory Model with Fuzzy Costs

Models that can handle demand variability and imprecise cost parameters are necessary for inventory management in uncertain circumstances. This paper offers a hybrid method that combines a probabilistic inventory model with fuzzy costs and Monte Carlo simulation. Fuzzy set theory is used to describe ambiguous or imprecise costs, including ordering, holding, and shortage costs. Monte Carlo simulation, on the other hand, is used to generate a variety of demands and scenarios based on known probability distributions. Because of this combination, inventory systems with uncertain parameters can be modelled more realistically. The model facilitates improved decision-making under uncertainty by examining simulated results under fuzzy cost conditions and providing insights into anticipated inventory costs. In situations where both randomness and vagueness are present, the suggested approach demonstrates increased accuracy and adaptability.