The present Covid-19 pandemic causes disruptions to markets and businesses in general, affecting the supply chain inventory system most significantly. This work investigates a hybrid payment inventory model considering inflation, cash discount, price-sensitive demand, and preservation technology investment for non-instantaneous deteriorating products. Due to the economic downturn, a hybrid payment scheme composed of multiple prepayments and a delay in payment is proposed to facilitate post Covid-19 recovery. The proposed model is one of the first models to consider a hybrid payment scheme in view of the Covid-19 pandemic, and to provide guidelines to retailers in planning the selling price, replenishment period, and preservation investment in view of the pandemic situation. The hybrid payment policy is suggested during the financial crisis to sustain orders from a retailer to the supplier and from the consumers to the retailer. During the supply disruptions, two cases for shortages and without shortages are studied. The nonlinear model is solved by Lingo 17 software. This study shows the effect of advance and delayed payments on the retailer’s total profit. It also shows that the total profit is extremely delicate to the inflation rate. The numerical examples illustrate the inventory model for different scenarios. Sensitivity analysis is carried out to provide managerial insights to management during post Covid-19 recovery.

目前的 Covid-19 大流行对市场和企业造成了总体破坏，对供应链库存系统的影响最为显着。这项工作研究了一种考虑通货膨胀、现金折扣、价格敏感需求和非瞬时变质产品保鲜技术投资的混合支付库存模型。由于经济不景气，提出了一种由多次预付款和延迟付款组成的混合支付方案，以促进 Covid-19 后的恢复。所提出的模型是针对 Covid-19 大流行考虑混合支付方案的首批模型之一，并为零售商提供了针对大流行情况规划售价、补货期和保全投资的指南。在金融危机期间建议采用混合支付政策，以维持从零售商到供应商以及从消费者到零售商的订单。在供应中断期间，研究了短缺和非短缺两种情况。非线性模型由Lingo 17软件求解。本研究显示了预付款和延迟付款对零售商总利润的影响。这也说明总利润对通货膨胀率极为敏感。数值示例说明了不同场景的库存模型。进行敏感性分析是为了在 Covid-19 后恢复期间为管理层提供管理见解。非线性模型由Lingo 17软件求解。本研究显示了预付款和延迟付款对零售商总利润的影响。这也说明总利润对通货膨胀率极为敏感。数值示例说明了不同场景的库存模型。进行敏感性分析是为了在 Covid-19 后恢复期间为管理层提供管理见解。非线性模型由Lingo 17软件求解。本研究显示了预付款和延迟付款对零售商总利润的影响。这也说明总利润对通货膨胀率极为敏感。数值示例说明了不同场景的库存模型。进行敏感性分析是为了在 Covid-19 后恢复期间为管理层提供管理见解。