论文标题
具有随机需求的新的广义新闻企业模型
A new generalized newsvendor model with random demand
论文作者
论文摘要
Newsvendor问题是库存管理方面的广泛研究主题。在这类库存问题中，短缺和多余成本被认为与损失数量成正比。但是，对于关键的商品或商品，库存决策是一个典型的例子，在这种例子中，过剩或短缺可能会导致比仅总成本更大的损失。在文献中没有太多讨论这样的问题。此外，大多数现有文献都认为需求分布是完全已知的。在本文中，我们建议对新闻供应商问题的概括，以解决较高或多余损失但程度相同的关键商品或商品。我们还假设，需求分布的参数未知。我们还根据需求的随机样本讨论最佳阶数数量的不同估计量。特别是，我们根据（i）完整样本和（ii）损坏的样本数据（即具有单一阶统计量）提供不同的估计器。我们还使用模拟偏差和平方误差（MSE）报告了估计器的比较。
Newsvendor problem is an extensively researched topic in inventory management. In this class of inventory problems, shortage and excess costs are considered to be proportional to the quantity lost. But, for critical goods or commodities, inventory decision is a typical example where, excess or shortage may lead to greater losses than merely the total cost. Such a problem has not been discussed much in the literature. Moreover, majority of the existing literature assumes the demand distribution to be completely known. In this paper, we propose a generalization of the newsvendor problem for critical goods or commodities with higher shortage or excess losses but of same degree. We also assume that, the parameters of the demand distribution are unknown. We also discuss different estimators of the optimal order quantity based on a random sample of demand. In particular, we provide different estimators based on (i) full sample and (ii) broken sample data (i.e with single order statistic). We also report comparison of the estimators using simulated bias and mean square error (MSE).
