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Pareto Set-based Ant Colony Optimization for Multi-Objective SurgeryScheduling Problem

机译:基于Pareto集的蚁群算法在多目标手术调度中的应用

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Surgery scheduling determines the individual surgery’s sequence and assigns required resources. This taskplays a decisive role in providing timely treatment for the patients while ensuring a balanced hospital resources’ utilization.Considering several real life constraints associated with multiple resources during the complete 3-stage surgery flow,a surgery scheduling model is presented with multiple objectives of minimizing makespan, minimizing overtime and balancingresource utilization. A Pareto sets based ant colony algorithm with corresponding ant graph, pheromone settingand update, and Pareto sets construction is proposed to solve the multi-objective surgery scheduling problem. A test casefrom MD Anderson Cancer Center is built and the scheduling result by three different approaches is compared. The casestudy shows that the Pareto set-based ACO for multi-objective proposed in this paper achieved good results in shorteningtotal end time, reducing nurses’ overtime and balancing resources’ utilization in general. It indicates the advantage by systematicallysurgery scheduling optimization considering multiple objectives related to different shareholders.
机译:手术时间表可确定各个手术的顺序并分配所需的资源。该任务在为患者提供及时治疗的同时,确保确保平衡地利用医院资源方面起着决定性的作用。考虑到在完整的三阶段手术流程中与多种资源相关联的几个现实生活中的制约因素,提出了一种具有最小化多个目标的手术调度模型有效期,最小化加班时间和平衡资源利用率。提出了一种基于Pareto集的蚁群算法,并结合相应的蚂蚁图,信息素设置和更新,构造了Pareto集,以解决多目标手术调度问题。建立了MD安德森癌症中心的测试案例,并比较了三种不同方法的调度结果。案例研究表明,本文提出的基于Pareto集合的多目标ACO在缩短总结束时间,减少护士的加班时间和平衡资源利用方面取得了良好的效果。它通过考虑与不同股东相关的多个目标的系统手术计划优化来表明其优势。

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