Raw Material Inventory Optimization for Steel Enterprises Under Price Uncertainty

Abstract

Due to the uncertainty of raw material prices, procurement and inventory costs account for a large proportion and are difficult to control in steel production. Aiming at the problem of raw material purchase and inventory in iron and steel enterprises with uncertain purchase price, in order to overcome the uncertainty of price and the conservatism of the solution, this paper establishes a robust optimization model for minimizing the total cost of purchase and inventory. Due to its inherent computational complexity, we introduced disturbance variables and employed duality theory to derive a tractable robust counterpart model. Finally, the model was applied to a real steel manufacturing enterprise to validate its effectiveness. The results indicate that, while the total cost of the robust model is slightly higher than that of the deterministic model for planning horizon of 3, 6, and 9, it becomes lower when the planning horizon is set to 12. For longer planning horizons, the robust model has a good effect on inventory and robustness under the condition of price fluctuations. Furthermore, we conducted an analysis of the impact of critical parameter thresholds on the total cost. The findings revealed that as the threshold value increases, the total cost also increases. Therefore, companies can select an appropriate threshold based on their specific circumstances to control costs.