  To determine the effectiveness of metaheuristic Differential Evolution
optimization strategy for inventory management (IM) in the context of
stochastic demand, this empirical study undertakes a thorough investigation.
The primary objective is to discern the most effective strategy for minimizing
inventory costs within the context of uncertain demand patterns. Inventory
costs refer to the expenses associated with holding and managing inventory
within a business. The approach combines a continuous review of IM policies
with a Monte Carlo Simulation (MCS). To find the optimal solution, the study
focuses on meta-heuristic approaches and compares multiple algorithms. The
outcomes reveal that the Differential Evolution (DE) algorithm outperforms its
counterparts in optimizing IM. To fine-tune the parameters, the study employs
the Latin Hypercube Sampling (LHS) statistical method. To determine the final
solution, a method is employed in this study which combines the outcomes of
multiple independent DE optimizations, each initiated with different random
initial conditions. This approach introduces a novel and promising dimension to
the field of inventory management, offering potential enhancements in
performance and cost efficiency, especially in the presence of stochastic
demand patterns.
