Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques

Article


Xia, H., Sun, Z., Wang, Y., Zhang, Z., Kamal, M. M., Jasimuddin, S. M. and Islam, N. 2023. Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques. International Journal of Production Research. In Press. https://doi.org/10.1080/00207543.2023.2267680
AuthorsXia, H., Sun, Z., Wang, Y., Zhang, Z., Kamal, M. M., Jasimuddin, S. M. and Islam, N.
Abstract

Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims.

JournalInternational Journal of Production Research
Journal citationIn Press
ISSN0020-7543
Year2023
PublisherTaylor & Francis
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1080/00207543.2023.2267680
Publication dates
Online01 Nov 2023
Publication process dates
Accepted25 Sep 2023
Deposited17 Nov 2023
Copyright holder© 2023, The Authors
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