There can be various causes of demand disruption like pandemic outbreaks, war situations, natural calamities, customer preference change, etc. Recently, the world witnessed one of the biggest disruptive events: the COVID-19 pandemic. Many times, when things seemed to be returning to normal, a new wave of pandemic arrived. Another disruptive event appeared in the form of Russia Ukraine war. As people avoid going out of their homes during such events, demand for a product is likely to go down. During the COVID-19 lockdown, demand for a normal product also vanished due to stringent rules imposed by the government. The novelty of the present work lies in collaborations of realistic aspects like demand disruption, both sudden fall and rise, time-dependent demand, stochastic two-parameter Weibull distribution deterioration rate, trade credit financing, and partial backlogged demand during stock out condition and also the validation of the model with real data sets in a two-warehouse inventory system, i.e., distributor possesses two warehouses one is owned by the distributor is called as an owned warehouse (OW) and another warehouse is hired on rent by the distributor called as a rented warehouse (RW). According to the author's knowledge, this work has not been completed previously. The proposed work determines an optimum lot size and optimum backlogged quantity to maximize profit under such a disruptive environment. According to the duration of the disruption, two distinct scenarios are developed to illustrate the impact of disruption duration on total average profit. The model is validated with numerical examples, and concavity is illustrated graphically. To derive critical theoretical managerial implications, a sensitivity analysis is conducted with respect to significant parameters. This study ends with a conclusion, followed by a discussion of the limitations of the current study and the potential directions for future research.

造成需求中断的原因可能有多种，例如流行病爆发、战争局势、自然灾害、客户偏好变化等。最近，世界目睹了最大的破坏性事件之一：COVID-19 大流行。很多时候，当一切似乎恢复正常时，新一波的流行病却来了。另一个破坏性事件以俄罗斯乌克兰战争的形式出现。由于人们在此类事件期间避免出门，对产品的需求可能会下降。在 COVID-19 封锁期间，由于政府实施的严格规定，对普通产品的需求也消失了。目前工作的新颖性在于现实方面的合作，如需求中断、突然下降和上升、依赖时间的需求、随机双参数威布尔分布恶化率、贸易信贷融资、缺货条件下的部分积压需求以及在双仓库库存系统中用真实数据集验证模型，即分销商拥有两个仓库，一个为分销商所有，称为自有仓库（OW），另一个仓库由分销商租用，称为租用的仓库（RW）。据笔者了解，这项工作此前尚未完成。拟议的工作确定了最佳批量和最佳积压数量，以在这种破坏性环境下实现利润最大化。根据中断的持续时间，开发了两种不同的场景来说明中断持续时间对平均利润总额的影响。该模型通过数值示例进行了验证，并以图形方式说明了凹性。为了得出关键的理论管理意义，对重要参数进行了敏感性分析。本研究以结论结束，然后讨论了当前研究的局限性和未来研究的潜在方向。