Zhang Yuwei, Li Zhenping, Zhao Yuwei
School of Information, Beijing Wuzi University, Beijing, China.
School of Economics and Management, Beijing University of Chemical Technology, Beijing, China.
Socioecon Plann Sci. 2023 Jun;87:101516. doi: 10.1016/j.seps.2023.101516. Epub 2023 Jan 22.
The outbreak of Coronavirus disease 2019 (COVID-19) highlights the importance of sufficient medical supplies stockpiling at the pre-event stage. In contrast, the potential disadvantages of maintaining adequate items at strategic locations (i.e., reserves) are considerable inventory-related costs. Unpredicted demand leads to a high degree of uncertainty. Efforts to mitigate the uncertainty should rely not only on prepositioning supplies at reserves but also on integrating various channels of medical materials. This paper proposes multi-mitigation strategies in medical supplies to ensure uninterrupted supply for hospitals and significant savings by introducing two-type suppliers, reserving and manufacturing suppliers. Thus, each hospital with uncertain demand is enabled to be served by various channels during pandemics: prepositioning in reserves, backups served by reserving suppliers, and medical commodities produced by manufacturing suppliers. Stochasticity is also incorporated into the raw materials available to produce. This research aims to develop an emergency response application that integrates preparedness action (reserve location, inventory level, and contract supplier's selection) with post-event operations (allocating medical materials from various channels). We formulate a two-stage stochastic mixed integer program to determine prepositioning strategy, including two-type suppliers' selection, and post-event allocation of multiple sources. A branch-and-Benders-cut method is developed for this problem and significantly outperforms both the classical Benders decomposition and Gurobi in the solution time. Different-sized test instances also verify the robustness of the proposed method. Based on a realistic and typical case study (inspired by the COVID-19 pandemic in Wuhan, China), significant savings, an increase in inventory utilization and an increase in demand fulfilment are obtained by our approach.
2019年冠状病毒病(COVID-19)的爆发凸显了在事件发生前阶段储备充足医疗物资的重要性。相比之下,在战略地点储备充足物资(即储备)的潜在缺点是与库存相关的成本相当高。不可预测的需求导致高度的不确定性。减轻这种不确定性的努力不仅应依赖于在储备地点预先储备物资,还应依赖于整合各种医疗物资渠道。本文提出了医疗物资的多重缓解策略,通过引入两类供应商(储备供应商和制造供应商)来确保医院的不间断供应并实现大幅节约。因此,在大流行期间,每个需求不确定的医院都能够通过各种渠道获得服务:储备物资、储备供应商提供的备用物资以及制造供应商生产的医疗商品。随机性也被纳入到可用于生产的原材料中。本研究旨在开发一种应急响应应用程序,将准备行动(储备地点、库存水平和合同供应商的选择)与事件发生后的运营(从各种渠道分配医疗物资)整合起来。我们制定了一个两阶段随机混合整数规划,以确定预先储备策略,包括两类供应商的选择以及多个来源的事件发生后分配。针对这个问题开发了一种分支 - 本德尔斯割平面法,在求解时间上显著优于经典的本德尔斯分解法和Gurobi。不同规模的测试实例也验证了所提出方法的稳健性。基于一个现实且典型的案例研究(受中国武汉COVID - 19大流行启发),我们的方法实现了大幅节约、库存利用率提高以及需求满足率提高。