Last-Mile Delivery of Malaria Prevention Products in the DRC An Inventory Management Model Under Supply Chain Disruptions

• 成果类型: Article; Early Access
• 署名作者: Ata, Baris; Lehman, Amy; Montgomery, Robert John
• 署名单位: University of Chicago; University of California System; University of California San Diego
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0177
• 发表日期: 2026
• 关键词: humanitarian logistics; or/ms research; HEALTH; INFORMATION; uncertainty; SYSTEM; relief; OPERATIONS; policies; NET

摘要

This paper studies inventory management policies to improve the last-mile delivery of healthcare products in remote settings prone to supply chain disruptions. It is motivated by the need for a reliable and cost-effective delivery system to resupply new mosquito-repellent products to combat malaria in the Lake Tanganyika region of the Democratic Republic of the Congo. The primary delivery methods used to supply villages along Lake Tanganyika can become inoperable during periods of flooding, machinery breakdowns, or armed conflict. To ensure continuous coverage, our model allows for secondary delivery methods with per unit costs and no fixed setup costs to be used during disruptions. In normal operating periods, our model allows for delivery using a large-capacity, fixed-setup-cost method with no variable costs. We model the changes in the operating environment between normal and disrupted states using a time-varying Markov chain, which allows for the incorporation of seasonal effects, namely, a distinct rainy season. Using an infinite-horizon, discounted-cost objective, we show that an (s, S) policy is optimal for managing inventory when in the normal operating state. In an extensive numerical study, we observe that a hybrid policy comprising an (s, S) policy in the normal state and base-stock policies in the disrupted states is optimal in the great majority of settings considered. We also derive several simpler heuristic policies and identify the parameter regimes in which they are effective substitutes for the hybrid and optimal policies.