Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

• 成果类型: Article
• 署名作者: Rana, Shraddha; Russell, Timothy; Boutilier, Justin J.; Goentzel, Jarrod
• 署名单位: Massachusetts Institute of Technology (MIT); University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478241231876
• 发表日期: 2024
• 页码: 682-700
• 关键词: Downstream fuel distribution; operational flow capacity; queuing theory; discrete event simulation; disaster intervention evaluation

摘要

Fuel is an essential commodity in society with an amplified role during disasters. Disasters often cause a spike in demand for fuel and impose physical and operational constraints on fuel distribution. Accordingly, public and private sector stakeholders seek disaster preparedness and response interventions to ensure an adequate supply of fuel for emergency activities. We develop models to quantify downstream operational flow capacity, i.e., the volume of fuel that can be distributed from bulk storage terminals to retail gas stations via tanker trucks. We do this for both steady state conditions and nonsteady state conditions during disasters. The operational flow capacity measures can serve as inputs to optimal relief distribution and resource allocation problems, as well as enable a cost-benefit assessment of interventions aimed at increasing system capacity. We identify the best interventions for each type of bulk storage terminal structure and determine how to prioritize bulk storage terminals for each type of intervention when resources are limited. As the downstream fuel distribution system from bulk storage terminals to retail gas stations is similar across regions, our methodology and insights are generalizable not only within the US but also in countries with comparable distribution systems. To demonstrate impact on practice, we present a case study drawn from the application of our methodology to support US federal disaster preparedness. More generally, we contribute to system level understanding of multiserver tandem cyclic queues with time-limited customers found in relief distribution systems, which is often overlooked in the disaster management literature.