A simulation-optimization approach for fractional, profitability-oriented inventory control under service-level type constraints
For logistics firms, this framework offers a practical method to align inventory management with profitability while maintaining service levels, though it is an incremental improvement over existing simulation-optimization approaches.
This study introduces a data-driven decision framework combining stochastic simulations with profit-oriented optimization to improve inventory control under uncertainty. Computational experiments show the approach improves both service consistency and financial yield.
Managing stock efficiently remains a core issue in modern logistics, where companies must reconcile cost efficiency with dependable service despite unpredictable market conditions. Conventional models often overlook the direct connection between investment in inventory and overall financial performance. This study introduces a data-driven decision framework that combines stochastic simulations with a profit-oriented optimization routine to enhance decision-making under uncertainty. The simulation stage generates performance estimates across multiple operating scenarios, providing realistic data on expenditures, revenues, and service reliability. These outcomes inform a fractional optimization process that searches for policies yielding the highest financial returns while maintaining required availability levels. The algorithm iteratively refines parameter values through feedback between simulated outcomes and optimization results, ensuring adaptability to dynamic enterprise systems. Computational experiments using representative business settings confirm that this approach improves both service consistency and financial yield. Overall, the framework demonstrates a practical, data-driven path for firms seeking to align operational responsiveness with sustainable profitability.