Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times

We study a stochastic outpatient appointment scheduling problem (SOASP) in which we need to design a schedule and an adaptive rescheduling (i.e., resequencing or declining) policy for a set of patients. Each patient has a known type and associated probability distributions of random service duration and random arrival time. Finding a provably optimal solution to this problem requires solving a multistage stochastic mixed‐integer program (MSMIP) with a schedule optimization problem solved at each stage, determining the optimal rescheduling policy over the various random service durations and arrival times. In recognition that this MSMIP is intractable, we first consider a two‐stage model (TSM) that relaxes the nonanticipativity constraints of MSMIP and so yields a lower bound. Second, we derive a set of valid inequalities to strengthen and improve the solvability of the TSM formulation. Third, we obtain an upper bound for the MSMIP by solving the TSM under the feasible (and easily implementable) appointment order (AO) policy, which requires that patients are served in the order of their scheduled appointments, independent of their actual arrival times. Fourth, we propose a Monte Carlo approach to evaluate the relative gap between the MSMIP upper and lower bounds. Finally, in a series of numerical experiments, we show that these two bounds are very close in a wide range of SOASP instances, demonstrating the near‐optimality of the AO policy. We also identify parameter settings that result in a large gap in between these two bounds. Accordingly, we propose an alternative policy based on neighbor‐swapping. We demonstrate that this alternative policy leads to a much tighter upper bound and significantly shrinks the gap.     

基于随机Petri网的维修质量管理组织结构建模与分析

简述了利用随机Petri网进行维修质量管理组织结构建模的方法。根据实际需求,建立了2种组织结构形式的仿真模型。以模型为基础,对维修质量管理组织结构的静态结构和动态行为进行了研究,解决了维修质量管理组织结构的优选问题,对组织结构设计具有一定的指导作用。     

武器系统效能评估数学模型描述

在武器系统效能评估中,武器系统的战技指标是评估武器系统效能的重要因素.对武器系统的可靠性和维修性进行了研究,并应用于可用性矩阵计算中.利用随机过程理论,建立了武器系统可信性矩阵和武器系统效能能力指数模型,最后建立了武器系统效能评估模型.     

基于随机Petri网的综合航电系统建模及分析

提出了一种利用随机Petri网对综合航电系统进行建模和性能分析的方法.首先,建立了综合航电系统数据总线传输系统的随机Petri网系统模型,通过系统模型进行综合航电系统的性能分析;其次,给出了航空电子综合系统数据总线传输基于固定主控端周期指令的总线性能分析结果;最后,通过两种分析结果的比较,得出了基于随机Petri网对航空电子综合系统数据总线传输建模分析是可行的结论.     

基于时滞分割法的具有leakage项时滞的离散型神经网络状态估计

摘要：研究一类具有leakage时滞的离散型神经网络的状态估计问题．通过构造新的Lyapunov泛函得到保证估计误差全局渐近稳定的充分条件,并通过求解一个线性矩阵不等式（LMI）得到状态估计器的增益矩阵．采用一种新的时滞分割方法将变时滞区间分割为多个子区间,使该结果在获得更小的保守性同时也降低了计算的复杂度．     

一类随机差分方程的解序列的最优停止规则

设报酬序列{x_(?),(?),n≥0}满足随机差分方程x_(n+1)=x_n+a_n+b_nε_(n+1)(ε_1,ε_2,…为白噪声序列)。本文讨论了用有限情形{x_n,0≤n≤N}的Snell包逼近无限情形{x_n,n≥0)的Snell包的条件,得到了x_n=E(x_n|(?))((?)=σ{ε_0,ε_1,…,ε_n},ε_0=0)的Snell包r_n的分解形式和最优停时存在的条件。最后讨论了最优停止规则的迭代计算法,并得出了迭代过程在有限步停止的充分条件。     

随机共振原理在强噪声背景信号检测中的应用

动态系统中的噪声常被认为是令人讨厌的东西 ,但在特定的非线性系统中 ,噪声的存在事实上能够增强微弱信号的检测能力 ,这种现象就是随机共振 (SR ,stochasticresonance) ,它在物理、工业技术和生物医学领域里具有广泛的应用潜力。给出了利用随机共振原理检测微弱周期信号的基本方法 ,并采用模拟的信号对该方法进行了分析与验证。结果表明 ,该方法简单、稳健、可靠 ,能把信噪比较低的周期信号从强背景噪声中可靠地提取出来 ,将在机械故障诊断领域展示诱人的前景     

随机 Petri 网的瓶颈及其最大处理能力

介绍了随机Ｐｅｔｒｉ网瓶颈的概念,给出了一般随机Ｐｅｔｒｉ网最大处理能力的数学模型,针对一类特殊结构的随机Ｐｅｔｒｉ网,导出了分析其最大处理能力和瓶颈的快速算法,最后给出了一个防空决策组织信息处理瓶颈与处理能力分析的实例。     

Inventory management with advance capacity information

An important aspect of supply chain management is dealing with demand and supply uncertainty. The uncertainty of future supply can be reduced if a company is able to obtain advance capacity information (ACI) about future supply/production capacity availability from its supplier. We address a periodic‐review inventory system under stochastic demand and stochastic limited supply, for which ACI is available. We show that the optimal ordering policy is a state‐dependent base‐stock policy characterized by a base‐stock level that is a function of ACI. We establish a link with inventory models that use advance demand information (ADI) by developing a capacitated inventory system with ADI, and we show that equivalence can only be set under a very specific and restrictive assumption, implying that ADI insights will not necessarily hold in the ACI environment. Our numerical results reveal several managerial insights. In particular, we show that ACI is most beneficial when there is sufficient flexibility to react to anticipated demand and supply capacity mismatches. Further, most of the benefits can be achieved with only limited future visibility. We also show that the system parameters affecting the value of ACI interact in a complex way and therefore need to be considered in an integrated manner. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011     

基于随机时间影响网络的联合火力打击方案评估

联合火力打击方案评估是作战筹划和指挥决策的核心问题之一,对于打击手段的选择、战法的运用等具有重要的影响,其难点是在不确定条件下有效地建立作战方案到使命目标达成效果之间的映射关系.作战仿真方法往往存在突出的低效率问题,而解析模型的方法难以充分反映目标体系的内部联系和整体效果.目前概率推理模型广泛应用于军事领域的作战方案评...