A stochastic appointment scheduling system on multiple resources with dynamic call-in sequence and patient no-shows for an outpatient clinic

This research focuses on the stochastic assignment system motivated by outpatient clinics, especially the physical therapy in rehabilitation service. The aim of this research is to develop a stochastic overbooking model to enhance the service quality as well as to increase the utilization of multiple resources, like therapy equipment in a physical therapy room, with the consideration of patients’ call-in sequence. The schedule for a single-service period includes a fixed number of blocks of equal length. When patients call, they are assigned to an appointment time for that block, and an existing appointment is not allowed to be changed. In each visit, a patient might require more than one resource and a probability of no-show. Two estimation methods were proposed for the expected waiting and overtime cost with multiple resources: Convolution Estimation Method and Joint Cumulative Estimation Method for the upper and lower bound value; respectively. A numerical example based on a physical therapy room was used to show that this stochastic model was able to schedule patients for better profitability compared with traditional appointment systems based on four prioritization rules. The workload in each appointment slot was more balanced albeit more patients were assigned to the first slot to fill up the empty room.     

Fast approximation algorithms for bi-criteria scheduling with machine assignment costs

We consider parallel machine scheduling problems where the processing of the jobs on the machines involves two types of objectives. The first type is one of two classical objective functions in scheduling theory: either the total completion time or the makespan. The second type involves an actual cost associated with the processing of a specific job on a given machine; each job-machine combination may have a different cost. Two bi-criteria scheduling problems are considered: (1) minimize the maximum machine cost subject to the total completion time being at its minimum, and (2) minimize the total machine cost subject to the makespan being at its minimum. Since both problems are strongly NP-hard, we propose fast heuristics and establish their worst-case performance bounds.     

一种面向HPC Cloud系统的在线作业调度算法

针对HPC Cloud中的作业之间对网络I/O资源的竞争问题,提出了一种基于动态规划算法、二分搜索算法和网络最大流算法的在线作业调度算法——DBMF算法.DBMF算法专注于物理节点上网络I/O的负载均衡,通过物理节点之间的网络I/O负载均衡来减少对网络I/O资源的竞争.通过与FCFS和RSF算法进行对比,DBMF调度算法有效提高了HPC Cloud的系统负载,减少了作业之间对网络I/O资源的竞争.     

基于预算的资源管理模型

针对现有批作业系统中的资源管理方式在资源使用公平性和合理性、作业服务质量(QoS)与实际需求存在较大差距的问题,提出一种基于经济学原理的资源管理模型——BB-RAM模型.模型通过预算机制来实现对计算资源管理和使用的宏观控制,最终达到资源使用最优化和保证作业服务质量的目的.基于实际作业流的仿真结果表明该模型的作业调度的作业延误率、效益值等QoS指标,以及平均响应时间等传统评价指标都优于传统调度策略.     

基于虚拟机迁移的虚拟机集群资源调度

针对虚拟机集群资源负载不平衡的问题,基于虚拟机迁移技术,提出了一种虚拟机集群资源调度策略,将虚拟机的资源进行了分类并用向量表示,描述了每一类资源的数量和负载的计量方法,用最佳适应算法寻找虚拟机迁移的目标主机,通过使用上限阈值和下限阈值约束资源利用率的方法,使负载平衡目标与节能目标相统一,引入最小迁移周期避免了虚拟机资源利用率不稳定带来的频繁迁移.该调度策略能为虚拟机中的应用提供透明的资源调度,并使虚拟机集群达到负载平衡和节能的双重目标.     

基于有限反馈MIMO下行多用户调度算法

针对基于有限反馈ZF预编码的MIMO多用户下行系统,提出了一种用户较少情况下的低复杂度调度算法.本算法以系统吞吐量的下限为目标函数,通过吞吐量下限最大化的办法选择用户,使系统获得多用户分集增益,从而增加了系统吞吐量.同时为了解决总反馈量随用户数线性增加的问题,提出了基于门限反馈的办法降低总的反馈速率.仿真结果表明：本算...     

一种新的处理能力优先的权值分配调度算法

针对加权最小连接数算法中服务节点权值不准确而直接影响负载均衡效率和整个集群系统性能的问题, 提出一种新的改进算法--处理能力优先的权值分配调度算法. 给出了改进算法的工作流程和要点, 分析并设计了算法的主要技术、 主要模块及原理. 实验结果表明: 该算法解决了LVS(Linux virtual server)负载调度的负载不平衡问题, 提高了LVS集群的性能和稳定性; 改进算法比加权最小连接数算法具有更优化的动态平衡性.     

具有学习效应的间歇批生产的单机排序问题

本文研究了目标函数为总完工时间,具有Dejong学习效应和遗忘效应的间歇批生产的单机排序问题.考虑了批与批之间没有学习效应的传递、有部分学习效应的传递和有总的学习效应传递3种模型.首先,在批与批之间没有学习效应传递的模型中,给出了复杂性为O(nlog n)的最优算法.其次,在批与批之间有部分学习效应传递的情形下,对批在...     

复杂桥梁工程施工动态的可视化仿真

鉴于复杂工程的不确定型网络计划和传统的施工管理工具不能形象展示大型桥梁施工过程,将4D可视技术应用于桥梁施工管理。首先,应用蒙特卡洛法对复杂不确定型网络计划进行仿真模拟;其次,将桥梁三维模型与模拟得到的施工进度计划、资源计划相结合,建立桥梁4D仿真模型;最后,依据4D仿真模型开发大型桥梁4D仿真系统。研究结果表明:应用该系统能可视查询和预测施工进度及资源使用情况;该系统能够输出施工工期、施工进度计划、关键路线、资源使用情况以及横道图、资源柱状图等仿真计算所得的文本和图形成果。