Evaluating appointment postponement in scheduling patients at a diagnostic clinic

Diagnostic clinics are among healthcare facilities that suffer from long waiting times which can worsen medical outcomes and increase patient no-shows. Reducing waiting times without significant capital investments is a challenging task. We tackle this challenge by proposing a new appointment scheduling policy that does not require significant investments for diagnostic clinics. The clinic in our study serves outpatients, inpatients, and emergency patients. Emergency patients must be seen on arrival, and inpatients must be given next day appointments. Outpatients, however, can be given later appointments. The proposed policy takes advantage of this by allowing the postponement of the acceptance of appointment requests from outpatients. The appointment scheduling process is modeled as a two-stage stochastic programming problem where a portion of the clinic capacity is allocated to inpatients and emergency patients in the first stage. In the second stage, outpatients are scheduled based on their priority classes. After a detailed analysis of the solutions obtained from the two-stage stochastic model, we develop a simple, non-anticipative policy for patient scheduling. We evaluate the performance of this proposed, easy-to-implement policy in a simulation study which shows significant improvements in outpatient indirect waiting times.     

基于任务和角色的分布式工作流安全模型

针对现有基于角色访问控制的缺陷和分布式工作流管理系统的特性,在传统的基于角色的访问控制模型中引入任务集(Tasks)、任务实例集(TaskInstances)和任务上下文(TaskContext)的概念,将传统的user role permission权限赋予结构修改为user role task permission权限赋予结构,建立了基于任务和角色的访问控制模型,给出了其形式化定义。该模型解决了传统的基于角色访问控制中的动态适应性差和最小权限约束假象的问题,用于分布式工作流管理系统,提高了安全性、实用性。     

Due‐window assignment with unit processing‐time jobs

In due‐window assignment problems, jobs completed within a designated time interval are regarded as being on time, whereas early and tardy jobs are penalized. The objective is to determine the location and size of the due‐window, as well as the job schedule. We address a common due‐window assignment problem on parallel identical machines with unit processing time jobs. We show that the number of candidate values for the optimal due‐window starting time and for the optimal due‐window completion time are bounded by 2. We also prove that the starting time of the first job on each of the machines is either 0 or 1, thus introducing a fairly simple, constant‐time solution for the problem. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004     

模糊合成算子在作战方案综合评估的应用研究

在分布式交互仿真中,根据仿真结果对作战方案进行综合评估,运用模糊综合评判的方法进行评判,并着重对合成算子的选择进行分析,确定乘与有界算子M(·, )为比较适合对作战方案评估的算子.     

An efficient metaheuristic for integrated scheduling and staffing IT projects based on a generalized minimum cost flow network

Scheduling IT projects and assigning the project work to human resources are an important and common tasks in almost any IT service company. It is particularly complex because human resources usually have multiple skills. Up to now only little work has considered IT‐specific properties of the project structure and human resources. In this article, we present an optimization model that simultaneously schedules the activities of multiple IT projects with serial network structures and assigns the project work to multiskilled internal and external human resources with different efficiencies. The goal is to minimize costs. We introduce a metaheuristic that decomposes the problem into a binary scheduling problem and a continuous staffing problem where the latter is solved efficiently by exploiting its underlying network structure. For comparison, we solve the mixed–binary linear program with a state–of–the–art commercial solver. The impacts of problem parameters on computation time and solution gaps between the metaheuristic and the solver are assessed in an experimental study. Our results show that the metaheuristic provides very favorable results in considerable less time than the solver for midsize problems. For larger problems, it shows a similar performance while the solver fails to return feasible solutions. © 2012 Wiley Periodicals, Inc. Naval Research Logistics 59: 111–127, 2012     

面向能耗有效高性能嵌入式微处理器的VLIW调度

为了降低功耗,目前能耗有效的嵌入式微处理器一般都采用分布式与层次化的寄存器文件结构。第一层的超小寄存器文件(TORF)的极小容量使得很多数据必须存放到第二层的通用寄存器文件(GRF)中,这给编译器带来了新的挑战。通过分析程序特征,提出了新的VLIW调度算法,通过在编译时对变量进行检测,在恰当的时机插入虚拟的copy操作并进行指令与通信调度,为对寄存器需求较大的全局变量与软流水变量构建了新的包含GRF的数据传输路由,将对TORF的压力转移到GRF中。实验结果表明,新的VLIW调度算法符合处理器的设计初衷。与不使用GRF相比,在程序性能只降低约8%的情况下,降低了约51%的寄存器访问能耗,43%的处理器能耗。最关键的是避免了程序员手工分配优化的难题。     

航天测控调度问题的拉格朗日启发式算法

通过分析航天测控调度问题的测控需求,建立了航天测控调度0-1整数规划模型,运用拉格朗日松弛方法对模型中的任务约束和设备约束进行了松弛,运用次梯度优化算法求得了航天测控调度问题上界,同时得到了决策变量对应的拉格朗日权重,可以作为决策变量在最优解中是否被调度的启发式信息,对拉格朗日权重进行分析,提出了求解问题可行解的拉格朗日启发式算法。最后,通过对两个场景的试验分析验证了拉格朗日启发式算法所求可行解的优越性。     

基于多智能体的反导传感器任务规划算法

首先探讨了反导作战中各类传感器资源的构成特点和功能划分,然后基于多智能体multy-agent system(MAS)的分布式人工智能技术提出了适应反导作战需求的传感器任务规划体系架构,既而深入分析了体系架构中各类agent的主要功能,最后给出了一个实体层传感器的任务规划流程和算法实例,为构建高效率、智能化的反导作战多传感器任务规划技术提供了一种有效的研究方法。     

基于DCSP的编队武器兼容性约束协调

编队武器兼容性约束协调,是一个典型的求解分布式约束满足问题的过程。针对这一特点,建立了编队武器兼容性约束满足问题模型,提出了一种基于异步回溯的分布式约束满足算法。该算法运用异步回溯获得一个初始可行解,然后以作战效能最优为原则增添新的方案,并进行约束一致性检查,最终得到满意的编队武器运用方案。仿真验证了算法的可行性。     

基于PCB技术的延时线设计方法

为了设计大功率低损耗的延时线,提出了用多股窄印制线并行绕制替代单股印制线绕制、在某些特定位置将并绕的多股印制线交叉换位的多层PCB螺旋电感的设计方法,并用该电感和电容模拟电缆的分布参数电感和电容制作延时线。通过多股并绕交叉换位的方式可以增加电感的有效截面积,减小电感的涡流损耗,有效提高电感的品质因数。测试结果表明:该螺旋电感的品质因数在短波频段内有较大提高。利用该螺旋电感制作的200ns延时线在通1A电流50 W功率时,延时性能好、驻波比小、衰减量小,满足大功率低损耗的要求。