Fast evaluation of a periodic review inventory policy

Abstract We consider a single-echelon inventory system facing stochastic demand controlled by the standard ( r , n Q , T ) batch ordering policy. While an exact algorithm for optimizing all three policy variables exists, excessive computational requirements hinder industrial applications, particularly in multi-product environments. Aiming at reducing computation time, we revisit the exact total average cost function. After establishing a new analytical property, we show that the exact ordering cost can be very accurately approximated by two convex functions. Using these, the original problem is decomposed in two separate sub-problems easier to solve. For each sub-problem we establish properties linking each solution with the classical EOQ, which lead to a fast heuristic search algorithm for the policy variables evaluation. Numerical comparisons with both the exact algorithm and two popular meta-heuristics demonstrate the excellent performance of the heuristic in terms of solution quality and run-time.