突发公共卫生事件应急医疗物资调度:基于AI技术的算法设计

IF 7 2区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Huosong Xia, Zelin Sun, Yuan Wang, Justin Zuopeng Zhang, Muhammad Mustafa Kamal, Sajjad M. Jasimuddin, Nazrul Islam
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引用次数: 0

摘要

针对突发公共卫生事件中医疗物资周转效率低、供需点调度不平衡等问题,基于人工智能技术,提出了一种新的大规模应急医疗物资调度算法。通过考虑紧急医疗物资的需求、实际分配和灾害现场的紧急程度,我们使用改进的基尼系数构建了公平指数。我们开发了一个具有最小基尼指数和调度时间的双目标优化模型。采用基于强化学习的异构蚁群算法求解Pareto边界。引入强化学习机制来更新和交换种群间的信息素,并设置奖励因子来调整信息素,提高算法收敛速度。通过将该算法的综合性能与超大容量评价指标进行比较,验证了该算法对大型EMSS问题的有效性。结果表明,该算法有效地缩短了EMSS问题的收敛时间,并易于摆脱局部最优。该算法解决了每个灾难点的需求差异影响公平分配的问题。本研究优化了针对公共卫生事件的早期EMSS计划,以尽量减少损失和伤亡,同时减轻灾害受害者的情绪困扰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques
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.
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来源期刊
International Journal of Production Research
International Journal of Production Research 管理科学-工程:工业
CiteScore
19.20
自引率
14.10%
发文量
318
审稿时长
6.3 months
期刊介绍: The International Journal of Production Research (IJPR), published since 1961, is a well-established, highly successful and leading journal reporting manufacturing, production and operations management research. IJPR is published 24 times a year and includes papers on innovation management, design of products, manufacturing processes, production and logistics systems. Production economics, the essential behaviour of production resources and systems as well as the complex decision problems that arise in design, management and control of production and logistics systems are considered. IJPR is a journal for researchers and professors in mechanical engineering, industrial and systems engineering, operations research and management science, and business. It is also an informative reference for industrial managers looking to improve the efficiency and effectiveness of their production systems.
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