论文标题
基于订单的贷款的多周期多生产随机库存问题
A multi-period multi-product stochastic inventory problem with order-based loan
论文作者
论文摘要
本文调查了一个多产品随机库存问题，其中一家受现金受限的在线零售商可以采用一些中国电子商务平台提供的基于订单的贷款，以加快其现金回收率以获得递延收入。我们首先为问题建立确定性模型，然后开发相应的随机编程模型，以最大程度地提高零售商在计划范围内的预期利润。客户需求的不确定性由方案树表示，而在场景树太大的情况下，则使用了降低方案技术来解决问题。我们根据在线商店中的真实数据爬网进行数值测试。结果表明，随机模型的表现优于确定性模型，尤其是当零售商受到现金限制较低时。此外，零售商倾向于选择基于订单的贷款的初始现金较小或面向长收据延迟长度时。
This paper investigates a multi-product stochastic inventory problem in which a cash-constrained online retailer can adopt order-based loan provided by some Chinese e-commerce platforms to speed up its cash recovery for deferred revenue. We first build deterministic models for the problem and then develop the corresponding stochastic programming models to maximize the retailers' expected profit over the planning horizon. The uncertainty of customer demand is represented by scenario trees, and a scenario reduction technique is used to solve the problem when the scenario trees are too large. We conduct numerical tests based on real data crawling from an online store. The results show that the stochastic model outperforms the deterministic model, especially when the retailer is less cash-constrained. Moreover, the retailer tends to choose using order-based loan when its initial available cash is small or facing long receipt delay length.
