A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management
<|reference_start|>A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management: Inventory management is a fundamental challenge in supply chain management. The challenge is compounded when the associated products have unpredictable demands. This study proposes an innovative optimization approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization. The proposed algorithm is tested with a real-life, unpredictable demand dataset to find the optimal stock to meet the business objective. The findings show a considerable improvement in inventory policy. This information is helpful for supply chain analytics decision-making, which increases productivity and profitability. This study further adds sensitivity analysis, considering the variation in demand and expected output in profit percentage. This paper makes a substantial contribution by presenting a simple yet robust approach to addressing the fundamental difficulty of inventory management in a dynamic business environment.<|reference_end|>
@article{maitra2024a,
    title={A System-Dynamic Based Simulation and Bayesian Optimization for
  Inventory Management},
    author={Sarit Maitra},
    journal={arXiv preprint arXiv:2402.10975},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10975},
    primaryClass={math.OC cs.CC}
}