The facts on the ground: Evaluating humanitarian fleet management policies using simulation

Gu, Liyi; Ryzhov, Ilya O.; Eftekhar, Mahyar

doi:10.1016/j.ejor.2020.12.019

Cited by 6 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With that intent, simulation models have been applied to humanitarian logistics (Pareja Yale et al., 2020). Problems such as distribution facility layout and operations evaluation, asset and funds management, fleet mix, purchase, sales, and scheduling, and collaboration strategies evaluation for the distribution of relief goods have been modeled (e.g., Besiou et al., 2014; Stauffer et al., 2018; Gu et al., 2021). These studies either do not model facilities, or consider just one supply facility, or do not model multiple cycles of distribution vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Operational Flow Capacity and Evaluating Disaster Interventions for Fuel Distribution

Rana,

Russell,

Boutilier

et al. 2024

Production and Operations Management

View full text Add to dashboard Cite

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 non-steady 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 multi-server tandem cyclic queues with time-limited customers found in relief distribution systems, which is often overlooked in the disaster management literature.

show abstract