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.

中文翻译：

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快速评估定期审查库存政策

摘要 我们考虑一个单梯队库存系统，该系统面临由标准 (r , n Q , T ) 批量订购策略控制的随机需求。虽然存在优化所有三个策略变量的精确算法，但过多的计算需求阻碍了工业应用，尤其是在多产品环境中。为了减少计算时间，我们重新审视了确切的总平均成本函数。在建立新的分析属性后，我们表明可以通过两个凸函数非常准确地近似精确排序成本。使用这些，原始问题被分解为两个更容易解决的独立子问题。对于每个子问题，我们建立将每个解决方案与经典 EOQ 联系起来的属性，这导致了用于策略变量评估的快速启发式搜索算法。