Hybrid simulation-optimization approach for planning relief-aid distribution with a real-world case study
Abstract
Purpose
Authorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.
Design/methodology/approach
A decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.
Findings
The comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.
Originality/value
The efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.
How to cite
Rezeq, M., Aouam, T., & Gailly, F. (2024). Hybrid simulation-optimization approach for planning relief-aid distribution with a real-world case study. Journal of Humanitarian Logistics and Supply Chain Management.
View at publisher
https://www.emerald.com/insight/content/doi/10.1108/JHLSCM-10-2023-0104/full/html