摘要
针对医院核磁共振(MRI)检查现有预约规则下资源利用率低的现状,提出了一种基于联合遗传和粒子群算法的资源调度(PSOGA)算法。基于现有MRI科室检查流程,引入多设备优先服务规则,建立了相应的MRI检查预约调度模型。为了求解这一调度模型,设计了采用联合遗传和粒子群算法的启发式资源调度算法。该算法通过在遗传算法寻优过程中引入粒子群算法,平衡全局搜索能力和局部搜索能力,有效解决了该问题中遗传算法早期收敛速度较慢的问题,而且能够获得较好的调度方案。仿真实验表明,与GA算法相比,PSOGA算法减少了优化完成时间,而且提高了患者体验和医院综合效益。
A scheduling optimization algorithm based on hybrid particle swarm optimization and genetic algorithm( PSOGA) is proposed to solve the problem that system resources are utilized with poor efficiency in the existing schedule method of magnetic resonance imaging( MRI). A reservation scheduling model of MRI examination is established using the multi-device priority service rule based on the existing inspection process.Owing to the complexity of the problem,a scheduling optimization algorithm based on hybrid particle swarm optimization and genetic algorithm is developed for solving the problem efficiently. The algorithm introduces particle swarm optimization in the optimization process of the genetic algorithm to balance capabilities of global and local searches,improve the rate of convergence in the early stage,and obtain a good scheduling solution.Simulation results show that the PSOGA algorithm reduces the scheduling time greatly and improves patients’ experience and comprehensive benefits of hospitals.
出处
《机电一体化》
2018年第9期55-60,共6页
Mechatronics
关键词
核磁共振
预约调度
优化算法
MRI examination
appointment scheduling
optimization algorithm
